The number of people around the world who have diabetes is about 422 million. Diabetes seriously affects the blood vessels in the retina, a disease called diabetic retinopathy (DR). The ophthalmologist examines signs through fundus images, such microaneurysm, exudates and neovascularisation and determines the suitable treatment for patient based on the condition. Currently, doctors require a long time and professional skills to detect DR. This study aimed to implement artificial intelligence (AI) to resolve the lack of current methods. This study implemented AI for detecting and classifying DR. AI uses deep learning, such the attention mechanism algorithm and AlexNet architecture. The attention mechanism algorithm focuses on detecting the pathological area in the fundus images, and AlexNet is used to classify DR into five levels based on the pathological area. This study also compared AlexNet architecture with and without attention mechanism. We obtained 344 fundus images from the Kaggle dataset, which contains normal, mild, moderate, severe and proliferative DR. The highest accuracy in this study is up to 91% and used the attention mechanism algorithm and AlexNet architecture. The experiment shows that our proposed method can provide results that can detect the pathological areas and effectively classify DR. Keywords: Artificial intelligence, Diabetic Retinopathy, Attention Mechanism, AlexNet
Artificial Intelligence Approach in Multiclass Diabetic Retinopathy Detection Using Convolutional Neural Network and Attention Mechanism
