Manuscript received December 19, 2023; revised January 26, 2024; accepted February 1, 2024; published July 5, 2024

Abstract—The integration of deep learning in medical image analysis is a transformative leap in healthcare, impacting diagnosis and treatment significantly. This scholarly review explores deep learning’s applications, revealing limitations in traditional methods while showcasing its potential. It delves into tasks like segmentation, classification, and enhancement, highlighting the pivotal roles of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Specific applications, like brain tumor segmentation and COVID-19 diagnosis, are deeply analyzed using datasets like NIH Clinical Center’s Chest X-ray dataset and BraTS dataset, proving invaluable for model training. Emphasizing high-quality datasets, especially in chest X-rays and cancer imaging, the article underscores their relevance in diverse medical imaging applications. Additionally, it stresses the managerial implications in healthcare organizations, emphasizing data quality and collaborative partnerships between medical practitioners and data scientists. This review article illuminates deep learning’s expansive potential in medical image analysis, a catalyst for advancing healthcare diagnostics and treatments.

Keywords—deep learning, machine learning, medical image analysis, high-quality medical image datasets

Cite: Ali H. Abdulwahhab, Noof T. Mahmood, Ali Abdulwahhab Mohammed, Indrit Myderrizi, and Mustafa Hamid Al-Jumaili, "A Review on Medical Image Applications Based on Deep Learning Techniques," Journal of Image and Graphics, Vol. 12, No. 3, pp. 215-227, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.