Multi-Disease Classification For Retinal Diseases Using Deep Learning Technique

Abstract

Diagnosis before the spread of retinal diseases is vital to prevent high level blindness and any sort of visual impairment. Many retinal diseases can be found via the fundus imaging which has a very important role in the observation and detection of various ophthalmic diseases. Most previous literature has focused their approaches on identifying individual diseases or a combination of 3-4 diseases like DR, MYA, ARMD, MH, ODC having major research. The eye is mostly affected by more than one underlying disease or disease marker, and uptil now most datasets had very few classes. Recently introduced RFMiD dataset, is one of the first datasets to provide 45 different classes of ophthalmic diseases. Hence making it possible to work towards automated multi-disease classification models which would provide great help to highlight this issue via clinical decision support systems integrated in the medical image diagnosis. Our work aimed to achieve higher accuracy than previous literature and to create an CDS application from the model in understanding and predicting multi retinal diseases. Deep learning models are excellent and have proven to be extremely effective in solving complex image processing problems. In addition, ensemble learning yields high generalization performance by reducing variance. Therefore, a synthesis of transfer, ensemble, and deep learning was used in this work to create an accurate and reliable model for multi retinal disease classification. To create the Multi Retinal Disease Classification Model (MRDCM) we used ensemble of EfficientNetB4 and EfficientNetV2S, with our final ensemble model giving promising results. In our evaluation, we scored an AUC of 0.973 which stands better than literature. Further our model selection is lighter than models used in literature. The model was tested on 27 main classes of RFMiD dataset for comparison with literature.