IoT based optical coherence tomography retinal images classification using OCT Deep Net2

Abstract

Machine learning algorithms gains prominence in health care sectors for disease diagnosis, classification and prediction. Deep learning architecture gains prominence in real time applications. This research work proposes a novel deep learning architecture, OCT Deep Net2 for the classification of optical coherence tomography images. Four class disease classification was performed in this research work and the proposed deep learning framework OCT Deep Net2 is an extension of OCT Deep Net1 comprising of 30 layers. The OCT Deep Net2 comprises of 50 layers and is termed as dense architecture, comprises of three recurrent modules. The performance validation reveals the efficiency of the OCT Deep Net2 architecture in terms of the performance metrics. Robust results were produced for batch size of 32 and 100 epochs with an accuracy of 98%. An IoT based system was implemented using Raspberry Pi B+ processor, the OCT Deep Net 2 algorithm was written in Python and executed on Google Colab.