by Zahra Mungloo-Dilmohamud 1,*, Maleika Heenaye-Mamode Khan 1, Khadiime Jhumka 1, Balkrish N. Beedassy 2, Noorshad Z. Mungloo 2 and Carlos Peña-Reyes3,4
1Faculty of Information, Communication and Digital Technologies, University of Mauritius, Réduit 80837, Mauritius
2Ministry of Health and Wellness, Quatre Bornes 72259, Mauritius
3School of Management and Engineering Vaud (HES-SO), University of Applied Sciences and Arts Western Switzerland Vaud, 1400 Yverdon-les-Bains, Switzerland
4CI4CB—Computational Intelligence for Computational Biology, SIB—Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
Appl. Sci.2022, 12, 5363. https://doi.org/10.3390/app12115363
Abstract
The incidence of diabetes in Mauritius is amongst the highest in the world. Diabetic retinopathy (DR), a complication resulting from the disease, can lead to blindness if not detected early. The aim of this work was to investigate the use of transfer learning and data augmentation for the classification of fundus images into five different stages of diabetic retinopathy. The five stages are No DR, Mild nonproliferative DR, Moderate nonproliferative DR, Severe nonproliferative DR and Proliferative. To this end, deep transfer learning and three pre-trained models, VGG16, ResNet50 and DenseNet169, were used to classify the APTOS dataset. The preliminary experiments resulted in low training and validation accuracies, and hence, the APTOS dataset was augmented while ensuring a balance between the five classes. This dataset was then used to train the three models, and the best three models were used to classify a blind Mauritian test datum. We found that the ResNet50 model produced the best results out of the three models and also achieved very good accuracies for the five classes. The classification of class-4 Mauritian fundus images, severe cases, produced some unexpected results, with some images being classified as mild, and therefore needs to be further investigated.