Diabetic Retinopathy Detection and Classification Using GoogleNet and Attention Mechanism Through Fundus Images

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Amnia Salma, Alhadi Bustamam, Anggun Rama Yudantha, Andi Arus Victor, Wibowo Mangunwardoyo

Abstract

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%.

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