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Applications of Deep Learning and Machine Learning in Medicine and Biology

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 June 2025 | Viewed by 453

Special Issue Editors

Prof. Dr. Byung-Gyu Kim

E-Mail Website
Guest Editor
Department of IT Engineering, Sookmyung Women’s University, Seoul 04310, Republic of Korea
Interests: image/video signal processing; pattern recognition; computer vision; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Ediz Polat

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Sciences, University of North Dakota, Grand Forks, North Dakota, ND 58202, USA
Interests: image processing; computer vision

Special Issue Information

Dear Colleagues,

Deep learning (DL) and machine learning (ML) techniques have resulted in significant advancements in medicine and biology, and these technologies are being applied across a variety of domains to improve diagnosis, treatment, and our understanding of diseases. Some of the key applications include medical imaging and analysis, drug discovery and development, disease diagnosis and risk prediction, natural language processing in medicine, robotics and surgery, biomedical signal processing, clinical decision support systems, and immunology and infectious diseases. As these technologies continue to evolve, they promise to revolutionize healthcare, offering more efficient, accurate, and personalized approaches to patient care, disease management, and scientific discovery. Thus, the research of these fields is valuable and continuously promises fresh research topics for researchers to explore.

We are pleased to invite submissions exploring recent research in the field of applications of deep learning and machine learning in medicine and biology to this Special Issue, which seeks the latest cutting-edge research, case studies, and reviews in the medical and biological contexts. We are particularly interested in papers that explore novel methodologies and applications, the integration of AI in clinical workflows, and the clinical impact of ML/DL-based systems.

Prof. Dr. Byung-Gyu Kim
Dr. Ediz Polat
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • artificial intelligence
  • machine learning
  • deep learning
  • medical imaging
  • medical AI applications
  • healthcare AI
  • biomedical informatics

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Published Papers (1 paper)

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Research

19 pages, 5434 KiB  
Article
A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection
by Murat Sarıateş and Erdal Özbay
Appl. Sci. 2025, 15(1), 225; https://doi.org/10.3390/app15010225 - 30 Dec 2024
Viewed by 319
Abstract
Background: Accurate and reliable classification models play a major role in clinical decision-making processes for prostate cancer (PCa) diagnosis. However, existing methods often demonstrate limited performance, particularly when applied to small datasets and binary classification problems. Objectives: This study aims to design a [...] Read more.
Background: Accurate and reliable classification models play a major role in clinical decision-making processes for prostate cancer (PCa) diagnosis. However, existing methods often demonstrate limited performance, particularly when applied to small datasets and binary classification problems. Objectives: This study aims to design a fine-tuned deep learning (DL) model capable of classifying PCa MRI images with high accuracy and to evaluate its performance by comparing it with various DL architectures. Methods: In this study, a basic convolutional neural network (CNN) model was developed and subsequently optimized using techniques such as L2 regularization, Tanh activation, dropout, and early stopping to enhance its performance. Additionally, a pyramid-type CNN architecture was designed to simultaneously evaluate both fine details and broader structures by combining low- and high-resolution information through feature maps extracted from different CNN layers. This approach enabled the model to learn complex features more effectively. For performance comparison, the developed fine-tuned enhanced pyramid network (FT-EPN) model was benchmarked against models such as Vgg16, Vgg19, Resnet50, InceptionV3, Densenet121, and Xception, which were trained using transfer learning (TL) techniques. It was also compared to next-generation models such as vision transformer (ViT) and MaxViT-v2. Results: The developed fine-tuned model achieved an accuracy rate of 96.77%, outperforming pre-trained TL models and next-generation models like ViT and MaxViT-v2. Among the TL models, Vgg19 achieved the highest accuracy rate at 92.74%. In comparison, ViT achieved an accuracy of 93.55%, while MaxViT-v2 achieved an accuracy of 95.16%. Conclusions: This study presents an optimized FT-EPN model to enhance the performance of DL models for PCa classification, offering a reference solution for future research. This model provides significant advantages in terms of classification accuracy and simplicity and has been evaluated as an effective solution in clinical applications. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Machine Learning in Medicine and Biology)
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