DRDM: Deep Learning Model for Diabetic Retinopathy Detection

Abstract

The application of Artificial Intelligence is being applied in the medical industry at a quick pace, and it is currently serving as the main source of support for clinical practice solutions. Clinical practice accuracy could be improved and costs could be decreased with the use of deep learning techniques. To diagnose Diabetic Retinopathy, an effective and dependable method for automatic screening must be identified. However, deep-learning models may face difficulties due to a lack of data in several medical fields. The Diabetic Retinopathy Detection Model (DRDM), a deep learning model, is proposed in this research to identify retinal images as either infected or uninfected. The data transformation approach is used to address the lack of Diabetic Retinopathy data, which helps prevent overfitting by doubling the data. The paper shows that building a highly complex model like EfficientNetB3 or VGG16 is not necessary to achieve high performance, where, the experiment's test results approved that the DRDM model outperforms such pre-trained models. Furthermore, it took much less time for the DRDM model to produce these results.