Automatic Grading of Diabetic Macular Edema Using Ensembled Dual Path EfficientNet With Augmentation

ABSTRACT The impact of rapid urbanisation, industrialisation, lack of awareness and lifestyle changes has resulted in an increase in the prevalence of diabetes and its complications such as diabetic macular edema (DME). It is the most common cause of blindness, characterised by an abnormal rise in the level of fluid in the macula. It affects the keenest vision in severe cases. In this paper, we propose an automatic grading of DME to help ophthalmologists diagnose the condition timely and early. The proposed system consists of an ensemble deep neural network using a residual convolution block followed by global average pooling and a dense layer. The proposed ensemble model consists of two models, namely model 1 and model 2. The proposed model has been evaluated on the publicly available benchmark ISBI IDRiD dataset. Our proposed model outperforms its competitive models in the IDRiD competition with an accuracy of 0.94. Moreover, it achieves a ROC‐AUC of 0.98. Gradient‐weighted Class Activation Mapping is also constructed to ensure that our proposed model is accurate.