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

TL;DR

This paper introduces a multitask deep learning model that simultaneously classifies and assesses the severity of diabetic retinopathy stages, leveraging a novel dependency-aware architecture and transfer learning for improved accuracy.

Contribution

The paper proposes a new multitask deep learning framework combining classification and regression models with dependency modeling for DR staging, outperforming existing methods.

Findings

Achieved weighted Kappa scores of 0.90 and 0.88 on APTOS and EyePACS datasets.

Developed a modified Squeeze Excitation DenseNet for DR detection.

Demonstrated superior performance over existing DR staging methods.

Abstract

This paper presents a multitask deep learning model to detect all the five stages of diabetic retinopathy (DR) consisting of no DR, mild DR, moderate DR, severe DR, and proliferate DR. This multitask model consists of one classification model and one regression model, each with its own loss function. Noting that a higher severity level normally occurs after a lower severity level, this dependency is taken into consideration by concatenating the classification and regression models. The regression model learns the inter-dependency between the stages and outputs a score corresponding to the severity level of DR generating a higher score for a higher severity level. 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 developed to implement this multitasking approach. The developed multitask model is then used to detect the five stages of DR by examining the two large Kaggle datasets of APTOS and EyePACS. A multitasking transfer learning model based on Xception network is also developed to evaluate the proposed approach by classifying DR into five stages. It is found that the developed model achieves a weighted Kappa score of 0.90 and 0.88 for the APTOS and EyePACS datasets, respectively, higher than any existing methods for detection of the five stages of DR