Benchmarking and Selecting Optimal Diabetic Retinopathy Detecting Machine Learning Model using Entropy and TOPSIS Method

Diabetic Retinopathy(DR) is a consequence of Diabetes Mellitus that manifests itself in the alteration of vascular tissue. When an alteration in the correct blood supply occurs, lesions such as micro-aneurysms, haemorrhages and exudates appear. A certain number of Machine Learning(ML) models are available to detect DR. Hence, a benchmarking method of these DR detection ML models can help to choose the best model based on some selected criteria. In this study, after pre-processing DR dataset, inception-v3 is used to extract the features from the images. Based on that, eight models are trained for detecting DR which are evaluated using ten metrics. Using those metrics as criteria, a decision matrix(DM) is formulated. Consequently, with respect to the weighted evaluation matrix, a Multi-Criteria Decision-Making(MCDM) method called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is assigned to benchmark the DR detection models where Entropy is used to calculate the individual weight of these criteria. The result shows that with the integration of Entropy and TOPSIS, the evaluation and determining problems associated with DR detection methods can be resolved. The AdaBoost classifier is selected as the best detecting model for DR detection which satisfies all the criteria in the best possible way for the data studied. As demonstrated here, the weights are generated by Entropy method which can be replaced by customized weights as needed.