Journal of Imaging

18 pages, 16537 KB

Open AccessArticle

E-CMCA and LSTM-Enhanced Framework for Cross-Modal MRI-TRUS Registration in Prostate Cancer

by Ciliang Shao, Ruijin Xue and Lixu Gu

J. Imaging 2025, 11(9), 292; https://doi.org/10.3390/jimaging11090292 - 27 Aug 2025

Accurate registration of MRI and TRUS images is crucial for effective prostate cancer diagnosis and biopsy guidance, yet modality differences and non-rigid deformations pose significant challenges, especially in dynamic imaging. This study presents a novel cross-modal MRI-TRUS registration framework, leveraging a dual-encoder architecture [...] Read more.

Accurate registration of MRI and TRUS images is crucial for effective prostate cancer diagnosis and biopsy guidance, yet modality differences and non-rigid deformations pose significant challenges, especially in dynamic imaging. This study presents a novel cross-modal MRI-TRUS registration framework, leveraging a dual-encoder architecture with an Enhanced Cross-Modal Channel Attention (E-CMCA) module and a LSTM-Based Spatial Deformation Modeling Module. The E-CMCA module efficiently extracts and integrates multi-scale cross-modal features, while the LSTM-Based Spatial Deformation Modeling Module models temporal dynamics by processing depth-sliced 3D deformation fields as sequential data. A VecInt operation ensures smooth, diffeomorphic transformations, and a FuseConv layer enhances feature integration for precise alignment. Experiments on the

μ

-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model significantly improves registration accuracy and performs robustly in both static 3D and dynamic 4D registration tasks. Experiments on the

μ

-RegPro dataset from the MICCAI 2023 Challenge demonstrate that our model achieves a DSC of 0.865, RDSC of 0.898, TRE of

2.278

, and RTRE of 1.293, surpassing state-of-the-art methods and performing robustly in both static 3D and dynamic 4D registration tasks. Full article

(This article belongs to the Special Issue Celebrating the 10th Anniversary of the Journal of Imaging)

25 pages, 4513 KB

Open AccessArticle

Dual-Filter X-Ray Image Enhancement Using Cream and Bosso Algorithms: Contrast and Entropy Optimization Across Anatomical Regions

by Antonio Rienzo, Miguel Bustamante, Ricardo Staub and Gastón Lefranc

J. Imaging 2025, 11(9), 291; https://doi.org/10.3390/jimaging11090291 - 26 Aug 2025

Abstract

This study introduces a dual-filter X-ray image enhancement technique designed to elevate the quality of radiographic images of the knee, breast, and wrist, employing the Cream and Bosso algorithms. Our quantitative analysis reveals significant improvements in bone, edge definition, and contrast (p [...] Read more.

This study introduces a dual-filter X-ray image enhancement technique designed to elevate the quality of radiographic images of the knee, breast, and wrist, employing the Cream and Bosso algorithms. Our quantitative analysis reveals significant improvements in bone, edge definition, and contrast (p < 0.001). The processing parameters are derived from the relationship between entropy metrics and the filtering parameter d. The results demonstrate contrast enhancements for knee radiographs and for wrist radiographs, while maintaining acceptable noise levels. Comparisons are made with CLAHE techniques, unsharp masking, and deep-learning-based models. This method is a reliable and computationally efficient approach to enhancing clinical diagnosis in resource-limited settings, thereby improving robustness and interpretability. Full article

(This article belongs to the Section Medical Imaging)

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17 pages, 45337 KB

Open AccessArticle

Contrastive Learning-Driven Image Dehazing with Multi-Scale Feature Fusion and Hybrid Attention Mechanism

by Huazhong Zhang, Jiaozhuo Wang, Xiaoguang Tu, Zhiyi Niu and Yu Wang

J. Imaging 2025, 11(9), 290; https://doi.org/10.3390/jimaging11090290 - 26 Aug 2025

Abstract

Image dehazing is critical for visual enhancement and a wide range of computer vision applications. Despite significant advancements, challenges remain in preserving fine details and adapting to diverse, non-uniformly degraded scenes. To address these issues, we propose a novel image dehazing method that [...] Read more.

Image dehazing is critical for visual enhancement and a wide range of computer vision applications. Despite significant advancements, challenges remain in preserving fine details and adapting to diverse, non-uniformly degraded scenes. To address these issues, we propose a novel image dehazing method that introduces a contrastive learning framework, enhanced by the InfoNCE loss, to improve model robustness. In this framework, hazy images are treated as negative samples and their clear counterparts as positive samples. By optimizing the InfoNCE loss, the model is trained to maximize the similarity between positive pairs and minimize that between negative pairs, thereby improving its ability to distinguish haze artifacts from intrinsic scene features and better preserving the structural integrity of images. In addition to contrastive learning, our method integrates a multi-scale dynamic feature fusion with a hybrid attention mechanism. Specifically, we introduce dynamically adjustable frequency band filters and refine the hybrid attention module to more effectively capture fine-grained, cross-scale image details. Extensive experiments on the RESIDE-6K and RS-Haze datasets demonstrate that our approach outperforms most existing methods, offering a promising solution for practical image dehazing applications. Full article

(This article belongs to the Special Issue Advances in Machine Learning for Computer Vision Applications)

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37 pages, 756 KB

Open AccessReview

From Fragment to One Piece: A Review on AI-Driven Graphic Design

by Xingxing Zou, Wen Zhang and Nanxuan Zhao

J. Imaging 2025, 11(9), 289; https://doi.org/10.3390/jimaging11090289 - 25 Aug 2025

Abstract

This survey offers a comprehensive overview of advancements in Artificial Intelligence in Graphic Design (AIGD), with a focus on the integration of AI techniques to enhance design interpretation and creative processes. The field is categorized into two primary directions: perception tasks, which involve [...] Read more.

This survey offers a comprehensive overview of advancements in Artificial Intelligence in Graphic Design (AIGD), with a focus on the integration of AI techniques to enhance design interpretation and creative processes. The field is categorized into two primary directions: perception tasks, which involve understanding and analyzing design elements, and generation tasks, which focus on creating new design elements and layouts. The methodology emphasizes the exploration of various subtasks including the perception and generation of visual elements, aesthetic and semantic understanding, and layout analysis and generation. The survey also highlights the role of large language models and multimodal approaches in bridging the gap between localized visual features and global design intent. Despite significant progress, challenges persist in understanding human intent, ensuring interpretability, and maintaining control over multilayered compositions. This survey aims to serve as a guide for researchers, detailing the current state of AIGD and outlining potential future directions. Full article

(This article belongs to the Section AI in Imaging)

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40 pages, 48075 KB

Open AccessArticle

Directional Lighting-Based Deep Learning Models for Crack and Spalling Classification

by Sanjeetha Pennada, Jack McAlorum, Marcus Perry, Hamish Dow and Gordon Dobie

J. Imaging 2025, 11(9), 288; https://doi.org/10.3390/jimaging11090288 - 25 Aug 2025

Abstract

External lighting is essential for autonomous inspections of concrete structures in low-light environments. However, previous studies have primarily relied on uniformly diffused lighting to illuminate images and faced challenges in detecting complex crack patterns. This paper proposes two novel algorithms that use directional [...] Read more.

External lighting is essential for autonomous inspections of concrete structures in low-light environments. However, previous studies have primarily relied on uniformly diffused lighting to illuminate images and faced challenges in detecting complex crack patterns. This paper proposes two novel algorithms that use directional lighting to classify concrete defects. The first method, named fused neural network, uses the maximum intensity pixel-level image fusion technique and selects the maximum intensity pixel values from all directional images for each pixel to generate a fused image. The second proposed method, named multi-channel neural network, generates a five-channel image, with each channel representing the grayscale version of images captured in the Right (R), Down (D), Left (L), Up (U), and Diffused (A) directions, respectively. The proposed multi-channel neural network model achieved the best performance, with accuracy, precision, recall, and F1 score of 96.6%, 96.3%, 97%, and 96.6%, respectively. It also outperformed the FusedNet and other models found in the literature, with no significant change in evaluation time. The results from this work have the potential to improve concrete crack classification in environments where external illumination is required. Future research focuses on extending the concepts of multi-channel and image fusion to white-box techniques. Full article

(This article belongs to the Section AI in Imaging)

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16 pages, 3972 KB

Open AccessArticle

Solar Panel Surface Defect and Dust Detection: Deep Learning Approach

by Atta Rahman

J. Imaging 2025, 11(9), 287; https://doi.org/10.3390/jimaging11090287 - 25 Aug 2025

Abstract

In recent years, solar energy has emerged as a pillar of sustainable development. However, maintaining panel efficiency under extreme environmental conditions remains a persistent hurdle. This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five [...] Read more.

In recent years, solar energy has emerged as a pillar of sustainable development. However, maintaining panel efficiency under extreme environmental conditions remains a persistent hurdle. This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust, Defective, Physical Damage, and Snow on photovoltaic surfaces. To build a robust foundation, a heterogeneous dataset of 8973 images was sourced from public repositories and standardized into a uniform labeling scheme. This dataset was then expanded through an aggressive augmentation strategy, including flips, rotations, zooms, and noise injections. A YOLOv11-based model was trained and fine-tuned using both fixed and adaptive learning rate schedules, achieving a mAP@0.5 of 85% and accuracy, recall, and F1-score above 95% when evaluated across diverse lighting and dust scenarios. The optimized model is integrated into an interactive dashboard that processes live camera streams, issues real-time alerts upon defect detection, and supports proactive maintenance scheduling. Comparative evaluations highlight the superiority of this approach over manual inspections and earlier YOLO versions in both precision and inference speed, making it well suited for deployment on edge devices. Automating visual inspection not only reduces labor costs and operational downtime but also enhances the longevity of solar installations. By offering a scalable solution for continuous monitoring, this work contributes to improving the reliability and cost-effectiveness of large-scale solar energy systems. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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18 pages, 1212 KB

Open AccessArticle

Part-Wise Graph Fourier Learning for Skeleton-Based Continuous Sign Language Recognition

by Dong Wei, Hongxiang Hu and Gang-Feng Ma

J. Imaging 2025, 11(8), 286; https://doi.org/10.3390/jimaging11080286 - 21 Aug 2025

Abstract

Sign language is a visual language articulated through body movements. Existing approaches predominantly leverage RGB inputs, incurring substantial computational overhead and remaining susceptible to interference from foreground and background noise. A second fundamental challenge lies in accurately modeling the nonlinear temporal dynamics and [...] Read more.

Sign language is a visual language articulated through body movements. Existing approaches predominantly leverage RGB inputs, incurring substantial computational overhead and remaining susceptible to interference from foreground and background noise. A second fundamental challenge lies in accurately modeling the nonlinear temporal dynamics and inherent asynchrony across body parts that characterize sign language sequences. To address these challenges, we propose a novel part-wise graph Fourier learning method for skeleton-based continuous sign language recognition (PGF-SLR), which uniformly models the spatiotemporal relations of multiple body parts in a globally ordered yet locally unordered manner. Specifically, different parts within different time steps are treated as nodes, while the frequency domain attention between parts is treated as edges to construct a part-level Fourier fully connected graph. This enables the graph Fourier learning module to jointly capture spatiotemporal dependencies in the frequency domain, while our adaptive frequency enhancement method further amplifies discriminative action features in a lightweight and robust fashion. Finally, a dual-branch action learning module featuring an auxiliary action prediction branch to assist the recognition branch is designed to enhance the understanding of sign language. Our experimental results show that the proposed PGF-SLR achieved relative improvements of 3.31%/3.70% and 2.81%/7.33% compared to SOTA methods on the dev/test sets of the PHOENIX14 and PHOENIX14-T datasets. It also demonstrated highly competitive recognition performance on the CSL-Daily dataset, showcasing strong generalization while reducing computational costs in both offline and online settings. Full article

(This article belongs to the Special Issue Advances in Machine Learning for Computer Vision Applications)

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23 pages, 2751 KB

Open AccessArticle

MSConv-YOLO: An Improved Small Target Detection Algorithm Based on YOLOv8

by Linli Yang and Barmak Honarvar Shakibaei Asli

J. Imaging 2025, 11(8), 285; https://doi.org/10.3390/jimaging11080285 - 21 Aug 2025

Abstract

Small object detection in UAV aerial imagery presents significant challenges due to scale variations, sparse feature representation, and complex backgrounds. To address these issues, this paper focuses on practical engineering improvements to the existing YOLOv8s framework, rather than proposing a fundamentally new algorithm. [...] Read more.

Small object detection in UAV aerial imagery presents significant challenges due to scale variations, sparse feature representation, and complex backgrounds. To address these issues, this paper focuses on practical engineering improvements to the existing YOLOv8s framework, rather than proposing a fundamentally new algorithm. We introduce MultiScaleConv-YOLO (MSConv-YOLO), an enhanced model that integrates well-established techniques to improve detection performance for small targets. Specifically, the proposed approach introduces three key improvements: (1) a MultiScaleConv (MSConv) module that combines depthwise separable and dilated convolutions with varying dilation rates, enhancing multi-scale feature extraction while maintaining efficiency; (2) the replacement of CIoU with WIoU v3 as the bounding box regression loss, which incorporates a dynamic non-monotonic focusing mechanism to improve localization for small targets; and (3) the addition of a high-resolution detection head in the neck–head structure, leveraging FPN and PAN to preserve fine-grained features and ensure full-scale coverage. Experimental results on the VisDrone2019 dataset show that MSConv-YOLO outperforms the baseline YOLOv8s by achieving a 6.9% improvement in mAP@0.5 and a 6.3% gain in recall. Ablation studies further validate the complementary impact of each enhancement. This paper presents practical and effective engineering enhancements to small object detection in UAV scenarios, offering an improved solution without introducing entirely new theoretical constructs. Future work will focus on lightweight deployment and adaptation to more complex environments. Full article

(This article belongs to the Section Computer Vision and Pattern Recognition)

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22 pages, 8764 KB

Open AccessArticle

Multi-Class Classification of Breast Cancer Subtypes Using ResNet Architectures on Histopathological Images

by Akshat Desai and Rakeshkumar Mahto

J. Imaging 2025, 11(8), 284; https://doi.org/10.3390/jimaging11080284 - 21 Aug 2025

Abstract

Breast cancer is a significant cause of cancer-related mortality among women around the globe, underscoring the need for early and accurate diagnosis. Typically, histopathological analysis of biopsy slides is utilized for tumor classification. However, it is labor-intensive, subjective, and often affected by inter-observer [...] Read more.

Breast cancer is a significant cause of cancer-related mortality among women around the globe, underscoring the need for early and accurate diagnosis. Typically, histopathological analysis of biopsy slides is utilized for tumor classification. However, it is labor-intensive, subjective, and often affected by inter-observer variability. Therefore, this study explores a deep learning-based, multi-class classification framework for distinguishing breast cancer subtypes using convolutional neural networks (CNNs). Unlike previous work using the popular BreaKHis dataset, where binary classification models were applied, in this work, we differentiate eight histopathological subtypes: four benign (adenosis, fibroadenoma, phyllodes tumor, and tubular adenoma) and four malignant (ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma). This work leverages transfer learning with ImageNet-pretrained ResNet architectures (ResNet-18, ResNet-34, and ResNet-50) and extensive data augmentation to enhance classification accuracy and robustness across magnifications. Among the ResNet models, ResNet-50 achieved the best performance, attaining a maximum accuracy of 92.42%, an AUC-ROC of 99.86%, and an average specificity of 98.61%. These findings validate the combined effectiveness of CNNs and transfer learning in capturing fine-grained histopathological features required for accurate breast cancer subtype classification. Full article

(This article belongs to the Special Issue AI-Driven Advances in Computational Pathology)

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31 pages, 5221 KB

Open AccessArticle

Dynamic–Attentive Pooling Networks: A Hybrid Lightweight Deep Model for Lung Cancer Classification

by Williams Ayivi, Xiaoling Zhang, Wisdom Xornam Ativi, Francis Sam and Franck A. P. Kouassi

J. Imaging 2025, 11(8), 283; https://doi.org/10.3390/jimaging11080283 - 21 Aug 2025

Abstract

Lung cancer is one of the leading causes of cancer-related mortality worldwide. The diagnosis of this disease remains a challenge due to the subtle and ambiguous nature of early-stage symptoms and imaging findings. Deep learning approaches, specifically Convolutional Neural Networks (CNNs), have significantly [...] Read more.

Lung cancer is one of the leading causes of cancer-related mortality worldwide. The diagnosis of this disease remains a challenge due to the subtle and ambiguous nature of early-stage symptoms and imaging findings. Deep learning approaches, specifically Convolutional Neural Networks (CNNs), have significantly advanced medical image analysis. However, conventional architectures such as ResNet50 that rely on first-order pooling often fall short. This study aims to overcome the limitations of CNNs in lung cancer classification by proposing a novel and dynamic model named LungSE-SOP. The model is based on Second-Order Pooling (SOP) and Squeeze-and-Excitation Networks (SENet) within a ResNet50 backbone to improve feature representation and class separation. A novel Dynamic Feature Enhancement (DFE) module is also introduced, which dynamically adjusts the flow of information through SOP and SENet blocks based on learned importance scores. The model was trained using a publicly available IQ-OTH/NCCD lung cancer dataset. The performance of the model was assessed using various metrics, including the accuracy, precision, recall, F1-score, ROC curves, and confidence intervals. For multiclass tumor classification, our model achieved 98.6% accuracy for benign, 98.7% for malignant, and 99.9% for normal cases. Corresponding F1-scores were 99.2%, 99.8%, and 99.9%, respectively, reflecting the model’s high precision and recall across all tumor types and its strong potential for clinical deployment. Full article

(This article belongs to the Section Medical Imaging)

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24 pages, 2959 KB

Open AccessArticle

From Detection to Diagnosis: An Advanced Transfer Learning Pipeline Using YOLO11 with Morphological Post-Processing for Brain Tumor Analysis for MRI Images

by Ikram Chourib

J. Imaging 2025, 11(8), 282; https://doi.org/10.3390/jimaging11080282 - 21 Aug 2025

Abstract

Accurate and timely detection of brain tumors from magnetic resonance imaging (MRI) scans is critical for improving patient outcomes and informing therapeutic decision-making. However, the complex heterogeneity of tumor morphology, scarcity of annotated medical data, and computational demands of deep learning models present [...] Read more.

Accurate and timely detection of brain tumors from magnetic resonance imaging (MRI) scans is critical for improving patient outcomes and informing therapeutic decision-making. However, the complex heterogeneity of tumor morphology, scarcity of annotated medical data, and computational demands of deep learning models present substantial challenges for developing reliable automated diagnostic systems. In this study, we propose a robust and scalable deep learning framework for brain tumor detection and classification, built upon an enhanced YOLO-v11 architecture combined with a two-stage transfer learning strategy. The first stage involves training a base model on a large, diverse MRI dataset. Upon achieving a mean Average Precision (mAP) exceeding 90%, this model is designated as the Brain Tumor Detection Model (BTDM). In the second stage, the BTDM is fine-tuned on a structurally similar but smaller dataset to form Brain Tumor Detection and Segmentation (BTDS), effectively leveraging domain transfer to maintain performance despite limited data. The model is further optimized through domain-specific data augmentation—including geometric transformations—to improve generalization and robustness. Experimental evaluations on publicly available datasets show that the framework achieves high mAP@0.5 scores (up to 93.5% for the BTDM and 91% for BTDS) and consistently outperforms existing state-of-the-art methods across multiple tumor types, including glioma, meningioma, and pituitary tumors. In addition, a post-processing module enhances interpretability by generating segmentation masks and extracting clinically relevant metrics such as tumor size and severity level. These results underscore the potential of our approach as a high-performance, interpretable, and deployable clinical decision-support tool, contributing to the advancement of intelligent real-time neuro-oncological diagnostics. Full article

(This article belongs to the Topic Machine Learning and Deep Learning in Medical Imaging)

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26 pages, 6425 KB

Open AccessArticle

Deep Spectrogram Learning for Gunshot Classification: A Comparative Study of CNN Architectures and Time-Frequency Representations

by Pafan Doungpaisan and Peerapol Khunarsa

J. Imaging 2025, 11(8), 281; https://doi.org/10.3390/jimaging11080281 - 21 Aug 2025

Abstract

Gunshot sound classification plays a crucial role in public safety, forensic investigations, and intelligent surveillance systems. This study evaluates the performance of deep learning models in classifying firearm sounds by analyzing twelve time–frequency spectrogram representations, including Mel, Bark, MFCC, CQT, Cochleagram, STFT, FFT, [...] Read more.

Gunshot sound classification plays a crucial role in public safety, forensic investigations, and intelligent surveillance systems. This study evaluates the performance of deep learning models in classifying firearm sounds by analyzing twelve time–frequency spectrogram representations, including Mel, Bark, MFCC, CQT, Cochleagram, STFT, FFT, Reassigned, Chroma, Spectral Contrast, and Wavelet. The dataset consists of 2148 gunshot recordings from four firearm types, collected in a semi-controlled outdoor environment under multi-orientation conditions. To leverage advanced computer vision techniques, all spectrograms were converted into RGB images using perceptually informed colormaps. This enabled the application of image processing approaches and fine-tuning of pre-trained Convolutional Neural Networks (CNNs) originally developed for natural image classification. Six CNN architectures—ResNet18, ResNet50, ResNet101, GoogLeNet, Inception-v3, and InceptionResNetV2—were trained on these spectrogram images. Experimental results indicate that CQT, Cochleagram, and Mel spectrograms consistently achieved high classification accuracy, exceeding 94% when paired with deep CNNs such as ResNet101 and InceptionResNetV2. These findings demonstrate that transforming time–frequency features into RGB images not only facilitates the use of image-based processing but also allows deep models to capture rich spectral–temporal patterns, providing a robust framework for accurate firearm sound classification. Full article

(This article belongs to the Section Image and Video Processing)

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17 pages, 3805 KB

Open AccessSystematic Review

The Genetics of Amyloid Deposition: A Systematic Review of Genome-Wide Association Studies Using Amyloid PET Imaging in Alzheimer’s Disease

by Amir A. Amanullah, Melika Mirbod, Aarti Pandey, Shashi B. Singh, Om H. Gandhi and Cyrus Ayubcha

J. Imaging 2025, 11(8), 280; https://doi.org/10.3390/jimaging11080280 - 19 Aug 2025

Abstract

Positron emission tomography (PET) has become a powerful tool in Alzheimer’s disease (AD) research by enabling in vivo visualization of pathological biomarkers. Recent efforts have aimed to integrate PET-derived imaging phenotypes with genome-wide association studies (GWASs) to better elucidate the genetic architecture underlying [...] Read more.

Positron emission tomography (PET) has become a powerful tool in Alzheimer’s disease (AD) research by enabling in vivo visualization of pathological biomarkers. Recent efforts have aimed to integrate PET-derived imaging phenotypes with genome-wide association studies (GWASs) to better elucidate the genetic architecture underlying AD. This systematic review examines studies that leverage PET imaging in the context of GWASs (PET-GWASs) to identify genetic variants associated with disease risk, progression, and brain region-specific pathology. A comprehensive search of PubMed and Embase databases was performed on 18 February 2025, yielding 210 articles, of which 10 met pre-defined inclusion criteria and were included in the final synthesis. Studies were eligible if they included AD populations, employed PET imaging alongside GWASs, and reported original full-text findings in English. No formal protocol was registered, and the risk of bias was not independently assessed. The included studies consistently identified APOE as the strongest genetic determinant of amyloid burden, while revealing additional significant loci including ABCA7 (involved in lipid metabolism and amyloid clearance), FERMT2 (cell adhesion), CR1 (immune response), TOMM40 (mitochondrial function), and FGL2 (protective against amyloid deposition in Korean populations). The included studies suggest that PET-GWAS approaches can uncover genetic loci involved in processes such as lipid metabolism, immune response, and synaptic regulation. Despite limitations including modest cohort sizes and methodological variability, this integrated approach offers valuable insight into the biological pathways driving AD pathology. Expanding PET-genomic datasets, improving study power, and applying advanced computational tools may further clarify genetic mechanisms and contribute to precision medicine efforts in AD. Full article

(This article belongs to the Section Medical Imaging)

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18 pages, 2639 KB

Open AccessArticle

Fundus Image-Based Eye Disease Detection Using EfficientNetB3 Architecture

by Rahaf Alsohemi and Samia Dardouri

J. Imaging 2025, 11(8), 279; https://doi.org/10.3390/jimaging11080279 - 19 Aug 2025

Abstract

Accurate and early classification of retinal diseases such as diabetic retinopathy, cataract, and glaucoma is essential for preventing vision loss and improving clinical outcomes. Manual diagnosis from fundus images is often time-consuming and error-prone, motivating the development of automated solutions. This study proposes [...] Read more.

Accurate and early classification of retinal diseases such as diabetic retinopathy, cataract, and glaucoma is essential for preventing vision loss and improving clinical outcomes. Manual diagnosis from fundus images is often time-consuming and error-prone, motivating the development of automated solutions. This study proposes a deep learning-based classification model using a pretrained EfficientNetB3 architecture, fine-tuned on a publicly available Kaggle retinal image dataset. The model categorizes images into four classes: cataract, diabetic retinopathy, glaucoma, and healthy. Key enhancements include transfer learning, data augmentation, and optimization via the Adam optimizer with a cosine annealing scheduler. The proposed model achieved a classification accuracy of 95.12%, with a precision of 95.21%, recall of 94.88%, F1-score of 95.00%, Dice Score of 94.91%, Jaccard Index of 91.2%, and an MCC of 0.925. These results demonstrate the model’s robustness and potential to support automated retinal disease diagnosis in clinical settings. Full article

(This article belongs to the Section Medical Imaging)

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35 pages, 11854 KB

Open AccessArticle

ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases

by Afraz Danish Ali Qureshi, Hassaan Malik, Ahmad Naeem, Syeda Nida Hassan, Daesik Jeong and Rizwan Ali Naqvi

J. Imaging 2025, 11(8), 278; https://doi.org/10.3390/jimaging11080278 - 18 Aug 2025

Abstract

Ocular disease (OD) represents a complex medical condition affecting humans. OD diagnosis is a challenging process in the current medical system, and blindness may occur if the disease is not detected at its initial phase. Recent studies showed significant outcomes in the identification [...] Read more.

Ocular disease (OD) represents a complex medical condition affecting humans. OD diagnosis is a challenging process in the current medical system, and blindness may occur if the disease is not detected at its initial phase. Recent studies showed significant outcomes in the identification of OD using deep learning (DL) models. Thus, this work aims to develop a multi-classification DL-based model for the classification of seven ODs, including normal (NOR), age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma (GLU), maculopathy (MAC), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR), using color fundus images (CFIs). This work proposes a custom model named the ocular disease detection model (ODDM) based on a CNN. The proposed ODDM is trained and tested on a publicly available ocular disease dataset (ODD). Additionally, the SMOTE Tomek (SM-TOM) approach is also used to handle the imbalanced distribution of the OD images in the ODD. The performance of the ODDM is compared with seven baseline models, including DenseNet-201 (R₁), EfficientNet-B0 (R₂), Inception-V3 (R₃), MobileNet (R₄), Vgg-16 (R₅), Vgg-19 (R₆), and ResNet-50 (R₇). The proposed ODDM obtained a 98.94% AUC, along with 97.19% accuracy, a recall of 88.74%, a precision of 95.23%, and an F1-score of 88.31% in classifying the seven different types of OD. Furthermore, ANOVA and Tukey HSD (Honestly Significant Difference) post hoc tests are also applied to represent the statistical significance of the proposed ODDM. Thus, this study concludes that the results of the proposed ODDM are superior to those of baseline models and state-of-the-art models. Full article

(This article belongs to the Special Issue Advances in Machine Learning for Medical Imaging Applications)

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18 pages, 7265 KB

Open AccessArticle

Automated Task-Transfer Function Measurement for CT Image Quality Assessment Based on AAPM TG 233

by Choirul Anam, Riska Amilia, Ariij Naufal, Eko Hidayanto, Heri Sutanto, Lukmanda E. Lubis, Toshioh Fujibuchi and Geoff Dougherty

J. Imaging 2025, 11(8), 277; https://doi.org/10.3390/jimaging11080277 - 18 Aug 2025

Abstract

This study aims to develop and validate software for the automatic measurement of the task-transfer function (TTF) based on the American Association of Physicists in Medicine (AAPM) Task Group (TG) 233. The software consists of two main stages: automatic placement of the region [...] Read more.

This study aims to develop and validate software for the automatic measurement of the task-transfer function (TTF) based on the American Association of Physicists in Medicine (AAPM) Task Group (TG) 233. The software consists of two main stages: automatic placement of the region of interest (ROI) within circular objects of the phantoms and calculating the TTF. The software was developed on four CT phantom types: computational phantom, ACR 464 CT phantom, AAPM CT phantom, and Catphan^® 604 phantom. Each phantom was tested with varying parameters, including spatial resolution level, slice thickness, and image reconstruction technique. The results of TTF were compared with manual measurements performed using ImQuest version 7.3.01 and iQmetix-CT version v1.2. The software successfully located ROIs at all circular objects within each phantom and measured accurate TTF with various contrast-to-noise ratios (CNRs) of all phantoms. The TTF results were comparable to those obtained with ImQuest and iQmetrix-CT. It was found that the TTF curves produced by the software are smoother than those produced by ImQuest. An algorithm for the automated measurement of TTF was successfully developed and validated. TTF measurement with our software is highly user-friendly, requiring only a single click from the user. Full article

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29 pages, 693 KB

Open AccessArticle

The Contribution of AIDA (Artificial Intelligence Dystocia Algorithm) to Cesarean Section Within Robson Classification Group

by Antonio Malvasi, Lorenzo E. Malgieri, Michael Stark, Edoardo Di Naro, Dan Farine, Giorgio Maria Baldini, Miriam Dellino, Murat Yassa, Andrea Tinelli, Antonella Vimercati and Tommaso Difonzo

J. Imaging 2025, 11(8), 276; https://doi.org/10.3390/jimaging11080276 - 16 Aug 2025

Abstract

Global cesarean section (CS) rates continue to rise, with the Robson classification widely used for analysis. However, Robson Group 2A patients (nulliparous women with induced labor) show disproportionately high CS rates that cannot be fully explained by demographic factors alone. This study explored [...] Read more.

Global cesarean section (CS) rates continue to rise, with the Robson classification widely used for analysis. However, Robson Group 2A patients (nulliparous women with induced labor) show disproportionately high CS rates that cannot be fully explained by demographic factors alone. This study explored how the Artificial Intelligence Dystocia Algorithm (AIDA) could enhance the Robson system by providing detailed information on geometric dystocia, thereby facilitating better understanding of factors contributing to CS and developing more targeted reduction strategies. The authors conducted a comprehensive literature review analyzing both classification systems across multiple databases and developed a theoretical framework for integration. AIDA categorized labor cases into five classes (0–4) by analyzing four key geometric parameters measured through intrapartum ultrasound: angle of progression (AoP), asynclitism degree (AD), head–symphysis distance (HSD), and midline angle (MLA). Significant asynclitism (AD ≥ 7.0 mm) was strongly associated with CS regardless of other parameters, potentially explaining many “failure to progress” cases in Robson Group 2A patients. The proposed integration created a combined classification providing both population-level and individual geometric risk assessment. The integration of AIDA with the Robson classification represented a potentially valuable advancement in CS risk assessment, combining population-level stratification with individual-level geometric assessment to enable more personalized obstetric care. Future validation studies across diverse settings are needed to establish clinical utility. Full article

(This article belongs to the Special Issue Clinical and Pathological Imaging in the Era of Artificial Intelligence: New Insights and Perspectives—2nd Edition)

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22 pages, 3234 KB

Open AccessArticle

A Lightweight CNN for Multiclass Retinal Disease Screening with Explainable AI

by Arjun Kumar Bose Arnob, Muhammad Hasibur Rashid Chayon, Fahmid Al Farid, Mohd Nizam Husen and Firoz Ahmed

J. Imaging 2025, 11(8), 275; https://doi.org/10.3390/jimaging11080275 - 15 Aug 2025

Abstract

Timely, balanced, and transparent detection of retinal diseases is essential to avert irreversible vision loss; however, current deep learning screeners are hampered by class imbalance, large models, and opaque reasoning. This paper presents a lightweight attention-augmented convolutional neural network (CNN) that addresses all [...] Read more.

Timely, balanced, and transparent detection of retinal diseases is essential to avert irreversible vision loss; however, current deep learning screeners are hampered by class imbalance, large models, and opaque reasoning. This paper presents a lightweight attention-augmented convolutional neural network (CNN) that addresses all three barriers. The network combines depthwise separable convolutions, squeeze-and-excitation, and global-context attention, and it incorporates gradient-based class activation mapping (Grad-CAM) and Grad-CAM++ to ensure that every decision is accompanied by pixel-level evidence. A 5335-image ten-class color-fundus dataset from Bangladeshi clinics, which was severely skewed (17–1509 images per class), was equalized using a synthetic minority oversampling technique (SMOTE) and task-specific augmentations. Images were resized to

150 \times 150

px and split 70:15:15. The training used the adaptive moment estimation (Adam) optimizer (initial learning rate of

1 \times 10^{- 4}

, reduce-on-plateau, early stopping),

ℓ_{2}

regularization, and dual dropout. The 16.6 M parameter network converged in fewer than 50 epochs on a mid-range graphics processing unit (GPU) and reached 87.9% test accuracy, a macro-precision of 0.882, a macro-recall of 0.879, and a macro-F1-score of 0.880, reducing the error by 58% relative to the best ImageNet backbone (Inception-V3, 40.4% accuracy). Eight disorders recorded true-positive rates above 95%; macular scar and central serous chorioretinopathy attained F1-scores of 0.77 and 0.89, respectively. Saliency maps consistently highlighted optic disc margins, subretinal fluid, and other hallmarks. Targeted class re-balancing, lightweight attention, and integrated explainability, therefore, deliver accurate, transparent, and deployable retinal screening suitable for point-of-care ophthalmic triage on resource-limited hardware. Full article

(This article belongs to the Section Medical Imaging)

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14 pages, 3502 KB

Open AccessArticle

Deep Learning-Based Nuclei Segmentation and Melanoma Detection in Skin Histopathological Image Using Test Image Augmentation and Ensemble Model

by Mohammadesmaeil Akbarpour, Hamed Fazlollahiaghamalek, Mahdi Barati, Mehrdad Hashemi Kamangar and Mrinal Mandal

J. Imaging 2025, 11(8), 274; https://doi.org/10.3390/jimaging11080274 - 15 Aug 2025

Abstract

Histopathological images play a crucial role in diagnosing skin cancer. However, due to the very large size of digital histopathological images (typically in the order of billion pixels), manual image analysis is tedious and time-consuming. Therefore, there has been significant interest in developing [...] Read more.

Histopathological images play a crucial role in diagnosing skin cancer. However, due to the very large size of digital histopathological images (typically in the order of billion pixels), manual image analysis is tedious and time-consuming. Therefore, there has been significant interest in developing Artificial Intelligence (AI)-enabled computer-aided diagnosis (CAD) techniques for skin cancer detection. Due to the diversity of uncertain cell boundaries, automated nuclei segmentation of histopathological images remains challenging. Automating the identification of abnormal cell nuclei and analyzing their distribution across multiple tissue sections can significantly expedite comprehensive diagnostic assessments. In this paper, a deep neural network (DNN)-based technique is proposed to segment nuclei and detect melanoma in histopathological images. To achieve a robust performance, a test image is first augmented by various geometric operations. The augmented images are then passed through the DNN and the individual outputs are combined to obtain the final nuclei-segmented image. A morphological technique is then applied on the nuclei-segmented image to detect the melanoma region in the image. Experimental results show that the proposed technique can achieve a Dice score of 91.61% and 87.9% for nuclei segmentation and melanoma detection, respectively. Full article

(This article belongs to the Section Medical Imaging)

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