Abstract
Featured Application The proposed approach can be applied to any of the Convolutional Neural Networks-based architecture to explain, evaluate and validate the model's decisions. Diabetic Retinopathy (DR) is a common complication associated with diabetes, causing irreversible vision loss. Early detection of DR can be very helpful for clinical treatment. Ophthalmologists' manual approach to DR diagnoses is expensive and time-consuming; thus, automatic detection of DR is becoming vital, especially with the increasing number of diabetes patients worldwide. Deep learning methods for analyzing medical images have recently become prevalent, achieving state-of-the-art results. Consequently, the need for interpretable deep learning has increased. Although it was demonstrated that the representation depth is beneficial for classification accuracy for DR diagnoses, model explainability is rarely analyzed. In this paper, we evaluated three state-of-the-art deep learning models to accelerate DR detection using the fundus images dataset. We have also proposed a novel explainability metric to leverage domain-based knowledge and validate the reasoning of a deep learning model's decisions. We conducted two experiments to classify fundus images into normal and abnormal cases and to categorize the images according to the DR severity. The results show the superiority of the VGG-16 model in terms of accuracy, precision, and recall for both binary and DR five-stage classification. Although the achieved accuracy of all evaluated models demonstrates their capability to capture some lesion patterns in the relevant DR cases, the evaluation of the models in terms of their explainability using the Grad-CAM-based color visualization approach shows that the models are not necessarily able to detect DR related lesions to make the classification decision. Thus, more investigations are needed to improve the deep learning model's explainability for medical diagnosis.