Diabetic Retinopathy Detection and Classification Using GoogleNet and Attention Mechanism Through Fundus Images - Amnia Salma, Alhadi Bustamam, Anggun Rama Yudantha, Andi Arus Victor, Wibowo Mangunwardoyo (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Diabetic Retinopathy (DR) is one of the leading causes of blindness globally for the patient who has Diabetes. There are about 422 million people who have Diabetes in the world. Image processing in the medical field is challenging in the Big Data era. The advantages of image processing are for detection and classification of disease based on the signs of fundus images. In this research, we used the Attention Mechanism algorithm and Googlenet for detection and classification of Diabetic Retinopathy into severity levels such as normal, mild, moderate, severe, and proliferative diabetic Retinopathy. The role of attention mechanism focuses on pathological area into fundus images, and the part of Googlenet focuses on classifying fundus images into Diabetic Retinopathy levels. We used 250 datasets for training data that we obtained from Kaggle, and the accuracy of this research gets excellent performance up to 97%.