Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy

Abstract

Early diagnosis and treatment of diabetic retinopathy (DR) can reduce the risk of vision loss. There are five stages of DR consisting of no DR, mild DR, moderate DR, severe DR, and proliferate DR. This paper presents a multitask deep learning model to detect all the five stages of DR more accurately than existing methods. The developed multitask model consists of one classification model and one regression model, each with its own loss function. After training the regression model and the classification model separately, the features extracted by these two models are concatenated and inputted to a multilayer perceptron network to classify the five stages of DR. A modified Squeeze Excitation Densely Connected deep neural network is also developed as part of this multitasking approach. The developed multitask model is applied to the two large Kaggle datasets of APTOS and EyePACS. The results obtained indicate that the developed multitask model achieved a weighted Kappa score of 0.90 and 0.88 for the APTOS and EyePACS datasets, respectively. In addition, the micro and macro average area under the receiver operating characteristic (ROC) curve was found to be 0.96, and 0.93, respectively, which are higher than existing methods for detecting the five stages of DR.INDEX TERMS Diabetic Retinopathy (DR), eye fundus images, five stages of diabetic retinopathy, multitasking deep neural network, squeeze excitation densely connected network.

Diabetic Retinopathy Diagnosis Based on RA-EfficientNet

“…To further prove the robustness of our RA-EfficientNet, we train and test the proposed RA-EfficientNet on the EyePACS dataset [23], from which we selected a total of 11,756 images. Table 9 shows a comparison of our RA-EfficientNet model, including three recent studies by Harihanth et al [24], Majumder et al [15] and He et al [25] based on the EyePACS dataset. The results clearly illustrate that RA-EfficientNet performs better for the classification of the five stages of DR.…”

“…Accuracy (%) Precision (%) F1 (%) Harihanth et al [24] 81.85 70.00 56.00 Majumder et al [15] 82.00 69.00 66.00 He et al [25] 86.…”

“…The accuracy of this model on the APTOS 2019 dataset was 82.54%. Majumder et al [15] combined the Xception model with a regression model to classify the five stages of DR, achieving an accuracy of 82%, 86% for the EyePACS and APTOS 2019 datasets respectively. Patel et al [16] used the pre-trained MobileNetV2 to classify the DR of the APTOS 2019 dataset, and obtained an accuracy of 91%.…”

Deep long and short term memory based Red Fox optimization algorithm for diabetic retinopathy detection and classification

“…The state‐of‐art comparison of accuracy is delineated in Figure 8. The state‐of‐art methods such as DCNN, 18 FL‐KmC, 17 SP, 20 Mt‐DL, 21 and proposed methods with different accuracy levels are achieved. Based on this investigation, we have obtained 97%, 96.67%, 98%, 88%, and 98.94% accuracy by using DCNN, FL‐KmC, SP, Mt‐DL, and the proposed method.…”

“…The experimental results delivered a 0.98% AUC value with larger computational complexities. The multitasking deep learning (Mt‐DL) model was introduced by Majumder et al 21 For a higher severity level, a higher score is generated that corresponds to the DR severity stage. The multitasking approach is implemented by developing a densely connected deep neural network.…”