Acta Scientific Medical Sciences (ASMS)(ISSN: 2582-0931)

Research Article Volume 4 Issue 12

Diabetic Retinopathy Detection-MobileNet Binary Classifier

Aruna Pavate*, Jay Mistry, Rahul Palve and Nirav Gami

Information Technology Department, Atharva College of Engineering, Mumbai University, India

*Corresponding Author: Aruna Pavate, Information Technology Department, Atharva College of Engineering, Mumbai University, India.

Received: September 28, 2020; Published: November 27, 2020



Background and Purpose: According to the International Diabetes Federation (IDF) the total number of people in India who are suffering from diabetes were around 50.8 million in the year 2010, and it will rise to 87.0 million by 2030. Diabetic Retinopathy is a one of the major complications that exhibits because of Type II diabetes. Diabetic Retinopathy causes blindness in the population of age mostly in between 20 to 64 years. In long term diabetic retinopathy blood vessels disturb the normal flow of fluid out the eye and that comes pressure on the eyeball and this may cause damage to nerves that emerge in glaucoma. Early detection of Diabetic retinopathy and treatment can significantly reduce the risk of vision loss.

Analysis Method: The manual diagnosis of Diabetic retinopathy by ophthalmologists takes time, effort and also includes more costs and can be misdiagnosed if computer aided diagnosis systems are not used. Recently, Deep Learning has become one of the most common methods to achieve high performance results in many areas, especially in medical image analysis and classification. This work addresses the problem of prediction of diabetic retinopathy in advance to avoid further complications in the near future. The proposed classifier was built using MobileNet architecture-a lightweight, mobile friendly architecture, which is trained using retinal fundus images from Aptos 2019 challenge dataset.

Findings: The proposed enhanced model gives an accuracy of 95% and precision, recall, f-1 scores are 0.95, 0.98 and 0.97 respectively. Presented results demonstrate that this model achieves promising results and can be deployed as an application for clinical testing. This work attempts to suggest the diabetic retinopathy complications in advance. The intention of the work is to help the practitioners not to replace the ophthalmologist.

Keywords: Diabetic Retinopathy; MobileNet; CNN



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Citation: Aruna Pavate., et al. “Diabetic Retinopathy Detection-MobileNet Binary Classifier". Acta Scientific Medical Sciences 4.12 (2020): 86-91.


Acceptance rate30%
Acceptance to publication20-30 days
Impact Factor0.851

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