MEDCnet: A Memory Efficient Approach for Processing High-Resolution Fundus Images for Diabetic Retinopathy Classification Using CNN

Abstract

Modern medical imaging equipment can capture very high-resolution images with detailed features. These high-resolution images have been used in several domains. Diabetic retinopathy (DR) is a medical condition where increased blood sugar levels of diabetic patients affect the retinal vessels of the eye. The usage of high-resolution fundus images in DR classification is quite limited due to Graphics processing unit (GPU) memory constraints. The GPU memory problem becomes even worse with the increased complexity of the current state-of-the-art deep learning models. In this paper, we propose a memory-efficient divide-and-conquer-based approach for training deep learning models that can identify both high-level and detailed low-level features from high-resolution images within given GPU memory constraints. The proposed approach initially uses the traditional transfer learning technique to train the deep learning model with reduced-sized images. This trained model is used to extract detailed low-level features from fixed-size patches of higher-resolution fundus images. These detailed features are then utilized for classification based on standard machine learning algorithms. We have evaluated our proposed approach using the DDR and APTOS datasets. The results of our approach are compared with different approaches, and our model achieves a maximum classification accuracy of 95.92% and 97.39% on the DDR and APTOS datasets, respectively. In general, the proposed approach can be used to get better accuracy by using detailed features from high-resolution images within GPU memory constraints.