Transfer Learning Techniques in Medical Image Classification

Medical image classification is a critical task in modern health care that aids in diagnosis, treatment planning, and patient care. However, it is often challenged by limited annotated data, domain shifts, and the complex nature of medical images. Transfer learning has emerged as a pivotal approach to mitigate these challenges by leveraging knowledge from related domains and large-scale datasets. This study provides a comprehensive exploration of transfer learning techniques in the context of medical image classification. The paper commences by elucidating the foundational concepts of transfer learning, highlighting its relevance to medical image analysis. Various transfer learning paradigms, including fine-tuning, feature extraction, and domain adaptation, are meticulously discussed, offering insights into their mechanisms and applicability. Ethical considerations and potential biases intrinsic to transfer learning models in the medical domain are also deliberated upon. The survey emphasizes the necessity of rigorously validating these models to ensure reliable and safe integration into clinical practice.