Diabetic retinopathy grading system based on transfer learning

Abstract

Much effort is being made by the researchers in order to detect and diagnose diabetic retinopathy (DR) accurately automatically. The disease is very dangerous as it can cause blindness suddenly if it is not continuously screened. Therefore, many computers aided diagnosis (CAD) systems have been developed to diagnose the various DR grades. Recently, many CAD systems based on deep learning (DL) methods have been adopted to get deep learning merits in diagnosing the pathological abnormalities of DR disease. In this paper, we present a full based-DL CAD system depending on multi-label classification. In the proposed DL CAD system, we present a customized efficientNet model in order to diagnose the early and advanced grades of the DR disease. Learning transfer is very useful in training small datasets. We utilized IDRiD dataset. It is a multilabel dataset. The experiments manifest that the proposed DL CAD system is robust, reliable, and deigns promising results in detecting and grading DR. The proposed system achieved accuracy (ACC) equals 86%, and the Dice similarity coefficient (DSC) equals 78.45%.

Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network

“…In this study, the preprocessing technique was simply to resize the image and implement augmentation. This study showed an accuracy value by the model of 86% [10].…”

“…Optimizers to be tested include SGD, Adam, Adamax, and Rmpsrop. The optimizer parameters are proposed based on research conducted by Maksoud et al [10], and Harikrishnan et al [11]. Some optimizers were used to train models on diabetic retinopathy datasets.…”

“…Based on the description of previous research [9], [10], [11], this study proposes the classification of Diabetic Retinopathy disease with CNN model architecture Efficientnet-B7. The EfficientNet-B7 Architecture Model is proposed because it has optimal performance compared to other architectures.…”

Multi-Scale Attention-Based Mechanism in Gradient Boosting Convolutional Neural Network for Diabetic Retinopathy Grade Classification

“…Table 3 aggregates and compares the recent research work on the IDRiD. In According to Table 3, CNN and the pre-trained models [10,12,13,26] surpassed the proposed model on the IDRiD. The proposed model obtained 4.1% greater than AlexNet model [10], 29.07% greater than CANet model [12], 8.17% greater than EffiecientNet B0 model [13] and 14.87% greater than GNN-based model [26].…”

“…A new Cross-disease Attention Network (CANet) [12] was designed to jointly grade DR and diabetic macular edema by finding the internal correlation among the diseases with only imagelevel supervision. A modified EfficientNet structure [13] was suggested to classify the early and advanced grades of the DR disease.…”