  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
