Open Access

ARTICLE

An IoT-Cloud Based Intelligent Computer-Aided Diagnosis of Diabetic Retinopathy Stage Classification Using Deep Learning Approach

by K. Shankar^1,*, Eswaran Perumal¹, Mohamed Elhoseny², Phong Thanh Nguyen³

1 Department of Computer Applications, Alagappa University, Karaikudi, 630001, India
2 Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
3 Department of Project Management, Ho Chi Minh City Open University, Ho Chi Minh City, 7000000, Vietnam

* Corresponding Author: K. Shankar. Email:

(This article belongs to the Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)

Computers, Materials & Continua 2021, 66(2), 1665-1680. https://doi.org/10.32604/cmc.2020.013251

Received 31 July 2020; Accepted 11 September 2020; Issue published 26 November 2020

Abstract

Diabetic retinopathy (DR) is a disease with an increasing prevalence and the major reason for blindness among working-age population. The possibility of severe vision loss can be extensively reduced by timely diagnosis and treatment. An automated screening for DR has been identified as an effective method for early DR detection, which can decrease the workload associated to manual grading as well as save diagnosis costs and time. Several studies have been carried out to develop automated detection and classification models for DR. This paper presents a new IoT and cloud-based deep learning for healthcare diagnosis of Diabetic Retinopathy (DR). The proposed model incorporates different processes namely data collection, preprocessing, segmentation, feature extraction and classification. At first, the IoT-based data collection process takes place where the patient wears a head mounted camera to capture the retinal fundus image and send to cloud server. Then, the contrast level of the input DR image gets increased in the preprocessing stage using Contrast Limited Adaptive Histogram Equalization (CLAHE) model. Next, the preprocessed image is segmented using Adaptive Spatial Kernel distance measure-based Fuzzy C-Means clustering (ASKFCM) model. Afterwards, deep Convolution Neural Network (CNN) based Inception v4 model is applied as a feature extractor and the resulting feature vectors undergo classification in line with the Gaussian Naive Bayes (GNB) model. The proposed model was tested using a benchmark DR MESSIDOR image dataset and the obtained results showcased superior performance of the proposed model over other such models compared in the study.

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