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J. Imaging
Special Issues
Advances in Machine Learning for Medical Imaging Applications
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Advances in Machine Learning for Medical Imaging Applications

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1284

Special Issue Editor

Dr. Eftychios E. Protopapadakis

E-Mail Website
Guest Editor
School of Information Sciences, University of Macedonia, 156 Egnatia Street, GR-546 36 Thessaloniki, Greece
Interests: computer vision; machine learning; semi-supervised learning; genetic algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, which aims to showcase the latest advancements in machine learning for medical imaging. The rapid progress of artificial intelligence (AI) has revolutionized medical imaging by enhancing disease detection, segmentation, and image reconstruction. Machine learning techniques, particularly deep learning and generative models, have significantly improved the accuracy and efficiency of diagnostic imaging. These advancements are critical in clinical applications, including early disease detection, personalized treatment planning, and automated workflow optimization. However, challenges such as data scarcity, model interpretability, and generalization across diverse patient populations remain active areas of research. This Special Issue aims to explore cutting-edge machine learning methodologies and their applications in medical imaging. By bringing together state-of-the-art research, we seek to advance the field and provide a deeper understanding of AI-driven medical diagnostics. The scope includes, but is not limited to, the following:

  • Novel AI architectures for medical image analysis;
  • Comparative evaluations of deep learning models in real-world medical imaging scenarios;
  • The integration of machine learning with existing imaging workflows;
  • Strategies for improving robustness, generalization, and explainability in AI-based healthcare solutions.

This Special Issue aligns with the journal’s scope by emphasizing the interdisciplinary nature of medical imaging, combining expertise from AI, computer vision, and medical sciences to drive impactful innovations.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Generative Models for Medical Imaging: Utilization of GANs and diffusion models for MRI/CT image synthesis and enhancement.
  • Automated Disease Detection and Segmentation: Development and comparison of SotA deep learning architectures for detecting diseases in medical scans.
  • Post-Processing and Ground Truth Refinement: AI-driven techniques to enhance segmentation accuracy for cancer and pathological region detection.
  • Brain Imaging and Neurological Disorders: Machine learning applications for autism spectrum disorder (ASD) detection, stroke analysis, and neurodegenerative disease diagnosis.
  • Multimodal Learning in Medical Imaging: Fusing MRI, CT, PET, and ultrasound data for improved diagnostic accuracy.
  • Few-Shot and Self-Supervised Learning: Overcoming data scarcity issues with novel training paradigms.
  • AI Explainability and Trustworthiness in Healthcare: Enhancing model interpretability and regulatory compliance in medical imaging AI.

Dr. Eftychios E. Protopapadakis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • medical imaging
  • deep learning
  • GANs
  • MRI reconstruction
  • segmentation
  • disease detection
  • multimodal learning
  • explainability
  • AI in healthcare

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

35 pages, 11854 KB  
Article
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
Viewed by 645
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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14 pages, 2224 KB  
Article
Evaluation of Transfer Learning Efficacy for Surgical Suture Quality Classification on Limited Datasets
by Roman Ishchenko, Maksim Solopov, Andrey Popandopulo, Elizaveta Chechekhina, Viktor Turchin, Fedor Popivnenko, Aleksandr Ermak, Konstantyn Ladyk, Anton Konyashin, Kirill Golubitskiy, Aleksei Burtsev and Dmitry Filimonov
J. Imaging 2025, 11(8), 266; https://doi.org/10.3390/jimaging11080266 - 8 Aug 2025
Viewed by 365
Abstract
This study evaluates the effectiveness of transfer learning with pre-trained convolutional neural networks (CNNs) for the automated binary classification of surgical suture quality (high-quality/low-quality) using photographs of three suture types: interrupted open vascular sutures (IOVS), continuous over-and-over open sutures (COOS), and interrupted laparoscopic [...] Read more.
This study evaluates the effectiveness of transfer learning with pre-trained convolutional neural networks (CNNs) for the automated binary classification of surgical suture quality (high-quality/low-quality) using photographs of three suture types: interrupted open vascular sutures (IOVS), continuous over-and-over open sutures (COOS), and interrupted laparoscopic sutures (ILS). To address the challenge of limited medical data, eight state-of-the-art CNN architectures—EfficientNetB0, ResNet50V2, MobileNetV3Large, VGG16, VGG19, InceptionV3, Xception, and DenseNet121—were trained and validated on small datasets (100–190 images per type) using 5-fold cross-validation. Performance was assessed using the F1-score, AUC-ROC, and a custom weighted stability-aware score (Scoreadj). The results demonstrate that transfer learning achieves robust classification (F1 > 0.90 for IOVS/ILS, 0.79 for COOS) despite data scarcity. ResNet50V2, DenseNet121, and Xception were more stable by Scoreadj, with ResNet50V2 achieving the highest AUC-ROC (0.959 ± 0.008) for IOVS internal view classification. GradCAM visualizations confirmed model focus on clinically relevant features (e.g., stitch uniformity, tissue apposition). These findings validate transfer learning as a powerful approach for developing objective, automated surgical skill assessment tools, reducing reliance on subjective expert evaluations while maintaining accuracy in resource-constrained settings. Full article
(This article belongs to the Special Issue Advances in Machine Learning for Medical Imaging Applications)
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