| Title |
Advancements in Deep Learning for Medical Image Analysis: Enhancing Diagnostic Accuracy and Disease Characterization |
| Authors |
(Indu P. K.) ; (G. Beni) ; (D. Rene Dev) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.2.215 |
| Keywords |
Deep learning; Medical imaging; Convolutional neural networks; Classification; Detection; Segmentation |
| Abstract |
With the expanding development of Deep Learning (DL) techniques, Medical Image Analysis (MIA) has become an active field of research. MIA typically refers to the utilization of various image modalities and techniques to obtain images of the human body, which can be used by medical experts for diagnosis and treatment. In this study, numerous advancements in MIA utilizing DL approaches for diverse pattern recognition tasks?such as segmentation, registration, categorization, detection/localization, and classification?are thoroughly surveyed. We discuss several recent research papers related to these tasks, covering applications such as liver lesion classification and segmentation, lung nodule detection and classification, brain tumor classification and detection, and breast cancer detection. A comparative description of these papers is provided in terms of organ, modality, dataset, model used, and limitations or needed improvements. This survey also describes several medical imaging modalities used in MIA and evaluates various challenges encountered in this domain. Finally, we discuss current trends for new researchers and medical instrument experts, encouraging them to leverage Deep Learning techniques for future advancements. |