Internet of Things and Cloud Enabled Hybrid Feature Extraction with Adaptive Neuro Fuzzy Inference System for Diabetic Retinopathy Diagnosis

Abstract

At present times, the diabetic retinopathy (DR) become high and it is required to design an Internet of Things (IoT) enabled DR diagnosis tool to assist the diagnosis process of remote patients. This study designs and develops IoT and cloud computing based Hybrid Feature Extraction (HFE) with Adaptive Neuro Fuzzy Inference System (ANFIS) for DR detection and classification model, abbreviated as HFE-ANFIS model. The proposed model initially captures the retinal fundus image of the patient using the IoT enabled head mounted camera and transmit the images to the cloud server, which executes the diagnosis process. The image preprocessing takes place using three stages namely color space conversion, filtering, and contrast enhancement. Next, segmentation process takes place using fuzzy c-means (FCM) model to identify the diseased portions in the fundus image. Then, HFE based feature extraction and ANFIS based classification processes are carried out to grade the different levels of DR. The performance validation of the HFE-ANFIS model takes place against MESSIDOR dataset and the results are investigated under different dimensions. The simulation outcome indicated that the HFE-ANFIS model has offered superior performance to other methods with the maximum average sensitivity of 94.55%, specificity of 96.41%, precision of 94.66% and accuracy of 95.97%.