Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images

Abstract

Diabetic retinopathy (DR) is one of the main causes of blindness in people around the world. Early diagnosis and treatment of DR can be accomplished by organizing large regular screening programs. Still, it is difficult to spot diabetic retinopathy timely because the situation might not indicate signs in the primary stages of the disease. Due to a drastic increase in diabetic patients, there is an urgent need for efficient diabetic retinopathy detecting systems. Auto-encoders, sparse coding, and limited Boltzmann machines were used as a few past deep learning (DL) techniques and features for the classification of DR. Convolutional Neural Networks (CNN) have been identified as a promising solution for detecting and classifying DR. We employ the deep learning capabilities of efficient net batch normalization (BNs) pre-trained models to automatically acquire discriminative features from fundus images. However, we successfully achieved F1 scores above 80% on all efficient net BNs in the EYE-PACS dataset (calculated F1 score for DeepDRiD another dataset) and the results are better than previous studies. In this paper, we improved the accuracy and F1 score of the efficient net BNs pre-trained models on the EYE-PACS dataset by applying a Gaussian Smooth filter and data augmentation transforms. Using our proposed technique, we have achieved F1 scores of 84% and 87% for EYE-PACS and DeepDRiD.

Figure 1

Our framework with all phases.

Figure 2

Fundus images of (I) no-DR, (II) mild-DR, (III) moderate-DR, (IV) severe-DR, (V) PDR.

Figure 3

Data loader.

Figure 4

The architecture of the efficient net model.

Figure 5

(a) EYE-PACS dataset (efficient net b6). (b) DeepDRiD dataset (efficient net b5).

Figure 6

(a) Plot the epoch average loss and Val AUC of the EYE-PACS dataset. (b) Plot the epoch average loss and Val AUC of the DeepDRiD dataset.