A multi-level fine-tuned deep learning based approach for binary classification of diabetic retinopathy

Abstract

Diabetes mellitus is a leading cause of diabetic retinopathy (DR), which results in retinal lesions and vision impairment. Untreated DR can lead to blindness, highlighting the need for early diagnosis and treatment. Unfortunately, DR has no cure, and treatments only help to preserve vision. Traditional manual diagnosis of DR retina fundus images by ophthalmologists is time-consuming, costly, and prone to errors. Computer-aided diagnosis methods, such as deep learning, have emerged as popular methods for improving diagnosis and reducing errors. Over the past decade, Convolutional Neural Networks (CNNs) have been shown to perform very well in medical image analysis due to their high ability to extract local features from images. Convolutional neural networks (CNNs) have shown great success in the processing of medical images, including DR color fundus images. In this paper, we proposed a multi-level fine-tuned deep learning based approach for the classification of diabetic retinopathy using three different pre-trained models including: DenseNet121, MobileNetV2, and Xception. The results are provided as classification accuracy, loss metrics, and the performance is compared with state-of-the-art works. The results indicates that the proposed Xception network surpassed its peers’ models as well as state-of-the-art methods by achieving the highest accuracy of 97.95% in binary classification of DR images.