A Quality Assessment Study of Deep Learning Techniques for Medical Image Diagnosis and Their Applications: A Systematic Literature Review

Medical imaging is one of the cornerstones of modern medicine, planning treatments, monitoring patient progress and aiding clinicians in diagnosing diseases such as tumors, cancer, and many others. With the rise of neural networks, especially deep learning (DL) approaches, significant advancements have been made in this domain. This systematic literature review intended to investigate and identify the latest implementations of DL algorithms for medical image processing by examining 294 peer-reviewed articles. We also explored the DL-based image segmentation methods, highlighting their advantages and limitations and the commonly used datasets in the field. Finally, we analyzed key challenges and outlined future research directions related to image segmentation. Our review reveals that convolutional neural networks, particularly U-Net and its variants, dominate the field, while deep neural networks show promising results enabling end-to-end learning, providing greater flexibility, and facilitating transfer learning. This study is conducted by defining the search process designed for execution based on a set of inclusion and exclusion criteria from major databases including IEEE explore, Scopus and DBLP.