Designing a learning model for mobile vision to detect diabetic retinopathy based on the improvement of MobileNetV2
by Hieu Nguyen; Vinh P. Tran; Vuong T. Pham; Hien D. Nguyen
International Journal of Digital Enterprise Technology (IJDET), Vol. 2, No. 1, 2022

Abstract: Diabetes is a leading cause for cases of blindness in the world. Early detection of diabetic retinopathy can prevent or delay diabetes-related blindness. Nowadays, a mobile phone is a necessary device for almost all people. It is usually used for some daily works. Thus, an application on a mobile phone for checking the eyes in order to diagnose diabetic retinopathy early is very helpful. In this article, a method for building the mobile application to detect diabetic retinopathy is studied based on MobileNetV2. This method is built on the improvements of depthwise separable convolution, combining the layers of linear bottleneck and inverted residuals. Those layers are not only effective to keep more useful features by using linear layers, but also do not increase the cost of computing a lot. The method was made more effective when it runs on mobile. The proposed method had been tested on two datasets EyePACs 2015 and APTOS 2019. The positive results of the experiment are emerging to build an application for detecting diabetic retinopathy which is used on mobile phones.

Online publication date: Mon, 22-Aug-2022

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