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Emerging Trends in Artificial Intelligence for Biomedical Image Analysis

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 1 June 2026 | Viewed by 23504

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Special Issue Editor

Prof. Dr. Francesco Mercaldo

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Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, 86100 Campobasso, Italy
Interests: medical image processing; XAI; multimodal learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has transformed biomedical image analysis, providing innovative solutions for disease diagnosis, treatment planning, and patient monitoring.

This Special Issue highlights the latest trends in AI-driven methodologies, such as deep learning, reinforcement learning, and hybrid approaches, which are tailored to the unique challenges of biomedical imaging and integrated with the Internet of Things (IoT) and advanced sensor technologies. IoT-enabled devices, along with real-time sensors, contribute vast streams of multi-modal data that AI systems can analyze to enhance precision in medical imaging applications. Noteworthy advancements in image segmentation, classification, and anomaly detection show how these technologies, combined with continuous sensor data, elevate precision and efficiency in diagnostics and remote patient monitoring.

Additionally, integrating explainable AI into clinical workflows is vital for interpreting complex model predictions, fostering trust and broader adoption in healthcare. This Special Issue also explores domain-specific applications, such as AI for radiology, pathology, and retinal image analysis, emphasizing the role of IoT and sensor fusion in enhancing diagnostic accuracy. Despite these advancements, challenges such as data privacy, model generalizability, and regulatory hurdles persist, particularly with IoT data security.

Moreover, this Special Issue will discuss how to integrate data from different imaging techniques (such as MRI, CT, PET, and ultrasound) to provide more comprehensive diagnostic information; to study the potential of multi-modal data fusion in improving the accuracy of disease detection and classification; and the application of AR and VR technologies in biomedical image analysis, particularly in surgical planning and educational training.

This Special Issue addresses these barriers by presenting novel frameworks, benchmarking studies, and emerging standards for ethical, IoT-integrated AI in healthcare. By featuring state-of-the-art research and comprehensive reviews, this Special Issue provides a roadmap for future research and development in AI and IoT-enabled biomedical image analysis, poised to revolutionize the field and ultimately improve patient outcomes.

Prof. Dr. Francesco Mercaldo
Guest Editor

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Keywords

computer vision
medical image analysis
deep learning
artificial intelligence
healthcare

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Published Papers (10 papers)

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Research

26 pages, 3904 KB

Open AccessArticle

AcneFormer: A Lesion-Aware and Noise-Robust CNN–Transformer for Acne Image Classification

by Yongtao Zhou and Kui Zhao

Sensors 2026, 26(8), 2533; https://doi.org/10.3390/s26082533 - 20 Apr 2026

Viewed by 422

Abstract

Convolutional neural networks (CNNs) have been widely used for acne image classification due to their effectiveness in capturing local texture of skin lesions. However, the locality of convolution operations limits their ability to model long-range dependencies. Vision Transformer (ViT) methods address this issue to some extent but their high computational complexity and reliance on large-scale pre-training present challenges. Although CNN–Transformer architecture alleviates this conflict to some extent, acne images present task-specific challenges, including indistinct lesion boundaries, subtle inter-class variations, and various facial interference factors. In this paper, we propose AcneFormer, a lesion-aware and noise-robust CNN–Transformer architecture for acne image classification. We introduce three modules especially for acne tasks: a Lesion Cue Enhancement (LCE) module to highlight discriminative multi-scale spatial patterns, a Cross-Layer Feature Transmission (CLFT) module to enhance cross-layer information flow in Transformers, and a Differential Semantic Denoising (DSD) module to suppress irrelevant responses during deep feature interaction. Extensive experiments show that AcneFormer outperforms several strong baselines. Ablation and external lesion-annotated analyses further show a consistent pattern: LCE mainly improves lesion-sensitive localization and class-balanced recognition, CLFT expands valid cross-depth lesion evidence, and DSD suppresses off-lesion semantic responses. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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24 pages, 10738 KB

Open AccessArticle

A Novel Convolutional Neural Network for Explainable Diabetic Retinopathy Detection and Grade Identification

by Simona Correra, Valeria Sorgente, Mario Cesarelli, Fabio Martinelli, Antonella Santone and Francesco Mercaldo

Sensors 2026, 26(8), 2510; https://doi.org/10.3390/s26082510 - 18 Apr 2026

Viewed by 282

Abstract

Diabetic retinopathy represents one of the leading causes of blindness worldwide, making early diagnosis essential for effective clinical intervention. We propose an explainable method aimed at automatically identifying the severity levels of diabetic retinopathy in retinal images using deep learning. The proposed method considers several convolutional neural network architectures, i.e., VGG16, StandardCNN, ResNet, CustomCNN, EfficientNet, MobileNet, and a novel architecture, i.e., FGNet, specifically designed and developed by the authors for diabetic retinopathy detection. The proposed network achieves an accuracy of 0.75 when trained for 10 epochs and 0.71 for 20 epochs. Explainability behind model prediction is further supported through Gradient-weighted Class Activation Mapping, providing visual insight into the learned decision-making process and potentially supporting early clinical assessment. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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26 pages, 8454 KB

Open AccessArticle

Real-Time Fluorescence-Based COVID-19 Diagnosis Using a Lightweight Deep Learning System

by Hui-Jae Bae, Jongweon Kim and Daesik Jeong

Sensors 2026, 26(1), 339; https://doi.org/10.3390/s26010339 - 5 Jan 2026

Viewed by 754

Abstract

The coronavirus is highly contagious, making rapid early diagnosis essential. Although deep learning-based diagnostic methods using CT or X-ray images have advanced significantly, they still face limitations in cost, processing time, and radiation exposure. In addition, for the possibility of real-time COVID-19 diagnosis, model lightweighting is required. This study proposes a lightweight deep learning model for COVID-19 diagnosis based on fluorescence images and demonstrates its applicability in embedded environments. To prevent data imbalance caused by noise and experimental variations, images were preprocessed using Gray Scale conversion, CLAHE, and Z-Score normalization to equalize brightness values. Among the tested architectures—VGG, ResNet, DenseNet, and EfficientNet—ResNet152 and VGG13 achieved the highest accuracies of 97.25% and 93.58%, respectively, and were selected for lightweighting. Layer-wise importance was calculated using an imprinting-based method, and less important layers were pruned. The pruned VGG13 maintained its accuracy while reducing model size by 18.9 MB and parameters by 4.2 M. ResNet152 (Prune 39) improved accuracy by 1% while reducing size by 161.5 MB and parameters by 40.22 M. The optimized model achieved 129.97 ms, corresponding to 7.69 frames per second (FPS) on an NPU(Furiosa AI Warboy), proving real-time COVID-19 diagnosis is feasible even on low-power edge devices. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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14 pages, 1487 KB

Open AccessArticle

YOLOv13-SwinTongue: Tongue Coating Diagnosis Using an Enhanced YOLOv13 with Swin Transformer

by Xiangqiang Yang, Jinchao Hao, Yonggang Wang, Yunfeng Man, Renjie Yang and Qinge Wu

Sensors 2026, 26(1), 219; https://doi.org/10.3390/s26010219 - 29 Dec 2025

Viewed by 930

Abstract

Tongue coating is a crucial diagnostic indicator in traditional Chinese medicine, intuitively reflecting the body’s physiological and pathological conditions. However, traditional visual inspection methods are highly susceptible to subjective bias, often resulting in diagnostic deviations and inconsistencies. To address these limitations, this study proposes an intelligent tongue coating diagnostic model based on an enhanced YOLOv13. The model integrates a hybrid architecture of swin transformer and YOLOv13, effectively capturing global contextual and local textural features for fine-grained recognition and analysis of tongue coating characteristics. Experimental results show that the enhanced model substantially outperforms the original YOLOv13 in fine-grained feature extraction and boundary localization, establishing a reliable foundation for the objectification, standardization, and intelligent advancement of tongue diagnosis in traditional Chinese medicine. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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17 pages, 1344 KB

Open AccessArticle

Lightweight Deep Learning Model for Classification of Normal and Abnormal Vasculature in Organoid Images

by Eunsu Yun, Jongweon Kim and Daesik Jeong

Sensors 2026, 26(1), 112; https://doi.org/10.3390/s26010112 - 24 Dec 2025

Viewed by 810

Abstract

Human organoids are 3D cell culture models that precisely replicate the microenvironment of real organs. In organoid-based experiments, assessing whether the internal vasculature has formed normally is essential for ensuring the reliability of experimental results. However, conventional vasculature assessment relies on manual inspection by researchers, which is time-consuming and prone to variability caused by subjective judgment. This study proposes a lightweight deep learning model for automatic classification of normal and abnormal vasculature in vascular organoid images. The proposed model is based on EfficientNet by replacing the activation function SiLU with ReLU and removing the Squeeze-and-Excitation (SE) blocks to reduce computational complexity. The dataset consisted of vascular organoid images obtained from co-culture experiments. Data augmentation and noise addition were performed to alleviate class imbalance. Experimental results show that the proposed Modified 3 models (B0, B1, B2) achieved accuracy of 0.90, 0.99, and 1.00, respectively, with corresponding inference speed of 51.1, 36.0, and 32.4 FPS on the CPU, demonstrating real-time inference capability and an average speed improvement of 70% compared to the original models. This study presents an efficient automated analysis framework that enables quantitative and reproducible vasculature assessment by introducing a lightweight model that maintains high accuracy and supports real-time processing. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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17 pages, 1515 KB

Open AccessArticle

CRTSC: Channel-Wise Recalibration and Texture-Structural Consistency Constraint for Anomaly Detection in Medical Chest Images

by Mingfu Xiong, Chong Wang, Hao Cai, Aziz Alotaibi, Saeed Anwar, Abdul Khader Jilani Saudagar, Javier Del Ser and Khan Muhammad

Sensors 2025, 25(21), 6722; https://doi.org/10.3390/s25216722 - 3 Nov 2025

Viewed by 1047

Abstract

Unsupervised medical image anomaly detection, which does not need any labels, holds a pivotal role in early disease detection for advancing human intelligent health, and it is among the prominent research endeavors in the realm of biomedical image analysis. Existing deep model-based methods mainly focus on feature selection and interaction, ignoring the relative position and shape uncertainty of the anomalies themselves, which play an important guiding role in disease diagnosis, hampering performance. To address this issue, our study introduces a novel and effective framework, termed CRTSC, which integrates a channel-wise recalibration module (CRM) along with the texture–structural consistency constraint (TSCC) for anomaly detection in medical chest images acquired from different sensors. Specifically, the CRM adjusts the weight of different medical image feature channels, which are used to establish spatial relationships among anomalous patterns, enhancing the network’s representation and generalization capabilities. The texture–structural consistency constraint is devoted to enhancing the anomaly’s structural (shape) definiteness via evaluating the loss function of similarity between two images and optimizing the model. The two collaborate in an end-to-end fashion to optimize and train the entire framework, thereby enabling anomaly detection in medical chest images. Extensive experiments conducted on the public ZhangLab and CheXpert datasets demonstrate that our method achieves a significant performance improvement compared with the state-of-the-art methods, offering a robust and generalizable solution for sensor-based medical imaging applications. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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23 pages, 9678 KB

Open AccessArticle

NeoNet: A Novel Deep Learning Model for Retinal Disease Diagnosis and Localization

by Valeria Sorgente, Simona Correra, Ilenia Verrillo, Mario Cesarelli, Fabio Martinelli, Antonella Santone and Francesco Mercaldo

Sensors 2025, 25(19), 6147; https://doi.org/10.3390/s25196147 - 4 Oct 2025

Viewed by 1331

Abstract

Retinal diseases are among the leading causes of vision impairment worldwide, and early detection is essential for enabling personalized treatments and preventing irreversible vision loss. In this paper, we propose a method aimed to identify and localize retinal conditions, i.e., Age-Related Macular Degeneration, Diabetic Retinopathy, and Choroidal Neovascularization, using explainable deep learning. For this purpose, we consider seven fine-tuned convolutional neural networks: MobileNet, LeNet, StandardCNN, CustomCNN, DenseNet, Inception, and EfficientNet. Moreover, we develop a novel architecture i.e., NeoNet, specifically designed for the detection of retinal diseases, achieving an accuracy of 99.5%. Furthermore, with the aim to provide explaianability behind the model decision, we highlight the most critical regions within retinal images influencing the predictions of the model. The obtained results show the ability of the model to detect pathological features, thereby supporting earlier and more accurate diagnosis of retinal diseases. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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26 pages, 4766 KB

Open AccessArticle

RetinoDeep: Leveraging Deep Learning Models for Advanced Retinopathy Diagnostics

by Sachin Kansal, Bajrangi Kumar Mishra, Saniya Sethi, Kanika Vinayak, Priya Kansal and Jyotindra Narayan

Sensors 2025, 25(16), 5019; https://doi.org/10.3390/s25165019 - 13 Aug 2025

Cited by 4 | Viewed by 2136

Abstract

Diabetic retinopathy (DR), a leading cause of vision loss worldwide, poses a critical challenge to healthcare systems due to its silent progression and the reliance on labor-intensive, subjective manual screening by ophthalmologists, especially amid a global shortage of eye care specialists. Addressing the pressing need for scalable, objective, and interpretable diagnostic tools, this work introduces RetinoDeep—deep learning frameworks integrating hybrid architectures and explainable AI to enhance the automated detection and classification of DR across seven severity levels. Specifically, we propose four novel models: an EfficientNetB0 combined with an SPCL transformer for robust global feature extraction; a ResNet50 ensembled with Bi-LSTM to synergize spatial and sequential learning; a Bi-LSTM optimized through genetic algorithms for hyperparameter tuning; and a Bi-LSTM with SHAP explainability to enhance model transparency and clinical trustworthiness. The models were trained and evaluated on a curated dataset of 757 retinal fundus images, augmented to improve generalization, and benchmarked against state-of-the-art baselines (including EfficientNetB0, Hybrid Bi-LSTM with EfficientNetB0, Hybrid Bi-GRU with EfficientNetB0, ResNet with filter enhancements, Bi-LSTM optimized using Random Search Algorithm (RSA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and a standard Convolutional Neural Network (CNN)), using metrics such as accuracy, F1-score, and precision. Notably, the Bi-LSTM with Particle Swarm Optimization (PSO) outperformed other configurations, achieving superior stability and generalization, while SHAP visualizations confirmed alignment between learned features and key retinal biomarkers, reinforcing the system’s interpretability. By combining cutting-edge neural architectures, advanced optimization, and explainable AI, this work sets a new standard for DR screening systems, promising not only improved diagnostic performance but also potential integration into real-world clinical workflows. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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30 pages, 2820 KB

Open AccessArticle

Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges

by Xiaolong Xiao, Jianfeng Zhang, Yuan Shao, Jialong Liu, Kaibing Shi, Chunlei He and Dexing Kong

Sensors 2025, 25(8), 2361; https://doi.org/10.3390/s25082361 - 8 Apr 2025

Cited by 19 | Viewed by 12184

Abstract

The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. Deep learning has recently emerged as a prominent field, playing a crucial role in medical image processing. This paper reviews ultrasound image and video segmentation methods based on deep learning techniques, summarizing the latest developments in this field, such as diffusion and segment anything models as well as classical methods. These methods are classified into four main categories based on the characteristics of the segmentation methods. Each category is outlined and evaluated in the corresponding section. We provide a comprehensive overview of deep learning-based ultrasound image segmentation methods, evaluation metrics, and common ultrasound datasets, hoping to explain the advantages and disadvantages of each method, summarize its achievements, and discuss challenges and future trends. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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23 pages, 1078 KB

Open AccessArticle

Enhanced U-Net for Infant Brain MRI Segmentation: A (2+1)D Convolutional Approach

by Lehel Dénes-Fazakas, Levente Kovács, György Eigner and László Szilágyi

Sensors 2025, 25(5), 1531; https://doi.org/10.3390/s25051531 - 28 Feb 2025

Cited by 1 | Viewed by 2400

Abstract

Background: Infant brain tissue segmentation from MRI data is a critical task in medical imaging, particularly challenging due to the evolving nature of tissue contrasts in the early months of life. The difficulty increases as gray matter (GM) and white matter (WM) intensities converge, making accurate segmentation challenging. This study aims to develop an improved U-net-based model to enhance the precision of automatic segmentation of cerebro-spinal fluid (CSF), GM, and WM in 10 infant brain MRIs using the iSeg-2017 dataset. Methods: The proposed method utilizes a U-net architecture with (2+1)Dconvolutional layers and skip connections. Preprocessing includes intensity normalization using histogram alignment to standardize MRI data across different records. The model was trained on the iSeg-2017 dataset, which comprises T1-weighted and T2-weighted MRI data from ten infant subjects. Cross-validation was performed to evaluate the model’s segmentation performance. Results: The model achieved an average accuracy of 92.2%, improving on previous methods by 0.7%. Sensitivity, precision, and Dice similarity scores were used to evaluate the performance, showing high levels of accuracy across different tissue types. The model demonstrated a slight bias toward misclassifying GM and WM, indicating areas for potential improvement. Conclusions: The results suggest that the U-net architecture is highly effective in segmenting infant brain tissues from MRI data. Future work will explore enhancements such as attention mechanisms and dual-network processing for further improving segmentation accuracy. Full article

(This article belongs to the Special Issue Emerging Trends in Artificial Intelligence for Biomedical Image Analysis)

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