Enhancing medical image classification with convolutional neural networks through transfer learning: A comprehensive review

Abstract

Medical imaging has become crucial in diagnostics, but manual analysis of the vast amount of data is cumbersome. Transfer learning, utilizing techniques like data augmentation, has addressed the challenges faced by deep learning methods like CNNs in this domain, improving classification accuracy. This study offers a comprehensive review of the applications and potential benefits of deep learning and transfer learning in medical image classification while also shedding light on prospective challenges and avenues for future research. After a comprehensive review and analysis of the literature, it can be inferred that transfer learning addresses the primary challenges deep learning faces in the medical field, enhancing its applicability. While deep learning has substantial potential, it grapples with issues of model "black-box" interpretability, where decisions made by the model are hard for clinicians to understand, and potential breaches of individual data security given the sensitive nature of medical records.