Enhancing Diabetic Retinopathy Classification for Small Sample Training Data Through Supervised Feature Attention: Transfer and Multitask Learning Approach

This study introduces a framework aimed at bolstering the effectiveness of deep learning models in image classification tasks, with a specialized focus on diabetic retinopathy detection. The proposed strategy employs transfer and multitask learning methods for the consistent extraction of insights and refinement of pre-established models. The capability of multitask learning to simultaneously acquire knowledge from numerous related tasks, leveraging shared information and inter-dependencies, leads to considerable performance boosts. Integrated into this process is the feature guideline learning task, which empowers the model to have an attention to abnormal areas. This task is initially trained on a small collection of labeled images to develop a custom pre-trained model, which later forms the basis for subsequent classification tasks. The effectiveness of this proposed approach is validated through tests on various data sets. It has been proven that our method contributes considerable advancements in image classification performance. This research offers valuable perspectives on the detection and classification of diabetic retinopathy, providing solutions to problems originating from limited data, and shows promising potential for improving patient care.