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Deep Learning in Medical Image Processing and Analysis
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Deep Learning in Medical Image Processing and Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 2807

Special Issue Editors

Prof. Dr. Jiann-Der Lee

E-Mail Website
Guest Editor
Department of Electrical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan
Interests: medical imaging processing; pattern recognition; computer visualization; VLSI design
Special Issues, Collections and Topics in MDPI journals
Dr. Jong-Chih Chien

E-Mail Website
Guest Editor
School of Informatics, Kainan University, Tao-Yuan 33857, Taiwan
Interests: digital image processing; artificial intelligence; machine vision; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, deep learning (DL) techniques have revolutionized the field of medical image processing, enabling automated analysis of medical images and enhancing the accuracy and efficiency of clinical diagnosis. The use of DL in medical image processing has significantly improved the detection, classification, and segmentation of various pathological conditions, leading to improved patient outcomes. Therefore, the application of DL in medical image processing in clinical settings is becoming increasingly common.

Prof. Dr. Jiann-Der Lee
Dr. Jong-Chih Chien
Guest Editors

Manuscript Submission Information

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Keywords

  • deep learning techniques for medical image analysis
  • AI-based detection and diagnosis of medical images using deep learning
  • AI-based approaches for image reconstruction and enhancement
  • AI-assisted decision making in medical imaging
  • medical image registration using deep learning
  • deep learning for multimodal medical image fusion

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Published Papers (3 papers)

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Research

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17 pages, 2718 KiB  
Article
Semantic Segmentation of Brain Tumors Using a Local–Global Attention Model
by Shuli Xing, Zhenwei Lai, Junxiong Zhu, Wenwu He and Guojun Mao
Appl. Sci. 2025, 15(11), 5981; https://doi.org/10.3390/app15115981 - 26 May 2025
Viewed by 233
Abstract
The distinctions between tumor areas and surrounding tissues are often subtle. Additionally, the morphology and size of tumors can vary significantly among different patients. These factors pose considerable challenges for the precise segmentation of tumors and subsequent diagnosis. Therefore, researchers are trying to [...] Read more.
The distinctions between tumor areas and surrounding tissues are often subtle. Additionally, the morphology and size of tumors can vary significantly among different patients. These factors pose considerable challenges for the precise segmentation of tumors and subsequent diagnosis. Therefore, researchers are trying to develop an automated and accurate segmentation model. Currently, many segmentation models in deep learning rely on Convolutional Neural Network or Vision Transformer. However, Convolution-based models often fail to deliver precise segmentation results, while Transformer-based models often require more computational resources. To address these challenges, we propose a novel hybrid model named Local–Global UNet Transformer. In our model, we introduce: (1) a semantic-oriented masked attention to enhance the feature extraction capability of the decoder; and (2) network-in-network blocks to increase channel modeling complexity in the encoder while reducing the parameter consumption associated with residual blocks. We evaluate our model on two public brain tumor segmentation datasets, and the experimental results demonstrate that our model achieves the highest average Dice score on the BraTS2024-GLI dataset and ranks second on the BraTS2023-GLI dataset. In terms of HD95, our model attains the lowest values on both datasets. Furthermore, the ablation study proves the effectiveness of our model design. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Processing and Analysis)
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15 pages, 1201 KiB  
Article
Perspective Transformation and Viewpoint Attention Enhancement for Generative Adversarial Networks in Endoscopic Image Augmentation
by Laimonas Janutėnas and Dmitrij Šešok
Appl. Sci. 2025, 15(10), 5655; https://doi.org/10.3390/app15105655 - 19 May 2025
Viewed by 250
Abstract
This study presents an enhanced version of the StarGAN model, with a focus on medical applications, particularly endoscopic image augmentation. Our model incorporates novel Perspective Transformation and Viewpoint Attention Modules for StarGAN that improve image classification accuracy in a multiclass classification task. The [...] Read more.
This study presents an enhanced version of the StarGAN model, with a focus on medical applications, particularly endoscopic image augmentation. Our model incorporates novel Perspective Transformation and Viewpoint Attention Modules for StarGAN that improve image classification accuracy in a multiclass classification task. The Perspective Transformation Module enables the generation of more diverse viewing angles, while the Viewpoint Attention Module helps focus on diagnostically significant regions. We evaluate the performance of our enhanced architecture using the Kvasir v2 dataset, which contains 8000 images across eight gastrointestinal disease classes, comparing it against baseline models including VGG-16, ResNet-50, DenseNet-121, InceptionNet-V3, and EfficientNet-B7. Experimental results demonstrate that our approach achieves better performance in all models for this eight-class classification problem, increasing accuracy on average by 0.7% on VGG-16 and 0.63% on EfficientNet-B7 models. The addition of perspective transformation capabilities enables more diverse examples to augment the database and provide more samples of specific illnesses. Our approach offers a promising solution for medical image generation, enabling effective training with fewer data samples, which is particularly valuable in medical model development where data are often scarce due to challenges in acquisition. These improvements demonstrate significant potential for advancing machine learning disease classification systems in gastroenterology and medical image augmentation as a whole. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Processing and Analysis)
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Review

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20 pages, 3843 KiB  
Review
Revolutionizing Periodontal Care: The Role of Artificial Intelligence in Diagnosis, Treatment, and Prognosis
by Giacomo Spartivento, Viviana Benfante, Muhammad Ali, Anthony Yezzi, Domenico Di Raimondo, Antonino Tuttolomondo, Antonio Lo Casto and Albert Comelli
Appl. Sci. 2025, 15(6), 3295; https://doi.org/10.3390/app15063295 - 18 Mar 2025
Viewed by 1253
Abstract
This review evaluates the application of artificial intelligence (AI), particularly neural networks, in diagnosing and staging periodontal diseases through radiographic analysis. Using a systematic review of 22 studies published between 2017 and 2024, it examines various AI models, including convolutional neural networks (CNNs), [...] Read more.
This review evaluates the application of artificial intelligence (AI), particularly neural networks, in diagnosing and staging periodontal diseases through radiographic analysis. Using a systematic review of 22 studies published between 2017 and 2024, it examines various AI models, including convolutional neural networks (CNNs), hybrid networks, generative adversarial networks (GANs), and transformer networks. The studies analyzed diverse datasets from panoramic, periapical, and hybrid imaging techniques, assessing diagnostic accuracy, sensitivity, specificity, and interpretability. CNN models like Deetal-Perio and YOLOv5 achieved high accuracy in detecting alveolar bone loss (ABL), with F1 scores up to 0.894. Hybrid networks demonstrate strength in handling complex cases, such as molars and vertical bone loss. Despite these advancements, challenges persist, including reduced performance in severe cases, limited datasets for vertical bone loss, and the need for 3D imaging integration. AI-driven tools offer transformative potential in periodontology by rivaling clinician performance, improving diagnostic consistency, and streamlining workflows. Addressing current limitations with large, diverse datasets and advanced imaging techniques will further optimize their clinical utility. AI stands poised to revolutionize periodontal care, enabling early diagnosis, personalized treatment planning, and better patient outcomes. Full article
(This article belongs to the Special Issue Deep Learning in Medical Image Processing and Analysis)
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