Enhancing Disease Diagnosis through Transfer Learning: Improving Accuracy and Generalization

Abstract

This study aims to investigate the effectiveness of transfer learning in the context of disease diagnosis. By leveraging pre-trained deep learning models on large-scale datasets, the objective is to enhance the accuracy and generalization of disease diagnosis models. This research explores the potential of transfer learning to improve diagnostic performance, particularly in cases where labeled data is limited. The study also examines the transferability of learned features across different diseases, considering the benefits of knowledge transfer from related domains. The goal is to develop a robust and efficient diagnostic framework that demonstrates improved accuracy and generalization in disease classification tasks.