{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:47:09Z","timestamp":1766137629796,"version":"3.41.2"},"reference-count":22,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2021,2,7]],"date-time":"2021-02-07T00:00:00Z","timestamp":1612656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJPCC"],"published-print":{"date-parts":[[2021,3,31]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to design an Internet-of-Things (IoT) architecture-based Diabetic Retinopathy Detection Scheme (DRDS) proposed for identifying Type-I or Type-II diabetes and to specifically advise the Type-II diabetic patients about the possibility of vision loss.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The proposed DRDS includes the benefits of automatic calculation of clip limit parameters and sub-window for making the detection process completely adaptive. It uses the advantages of extended 5 \u00d7 5 Sobels operator for estimating the maximum edges determined through the convolution of 24 pixels with eight templates to achieve 24 outputs corresponding to individual pixels for finding the maximum magnitude. It enhances the probability of connecting pixels in the vascular map with its closely located neighbourhood points in the fundus images. Then, the spatial information and kernel of the neighbourhood pixels are integrated through the Robust Semi-supervised Kernelized Fuzzy Local information C-Means Clustering (RSKFL-CMC) method to attain significant clustering process.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The results of the proposed DRDS architecture confirm the predominance in terms of accuracy, specificity and sensitivity. The proposed DRDS technique facilitates superior performance at an average of 99.64% accuracy, 76.84% sensitivity and 99.93% specificity.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>DRDS is proposed as a comfortable, pain-free and harmless diagnosis system using the merits of Dexcom G4 Plantinum sensors for estimating blood glucose level in diabetic patients. It uses the merits of RSKFL-CMC method to estimate the spatial information and kernel of the neighborhood pixels for attaining significant clustering process.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The IoT architecture comprises of the application layer that inherits the DR application enabled Graphical User Interface (GUI) which is combined for processing of fundus images by using MATLAB applications. This layer aids the patients in storing the capture fundus images in the database for future diagnosis.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Social implications<\/jats:title>\n<jats:p>This proposed DRDS method plays a vital role in the detection of DR and categorization based on the intensity of disease into severe, moderate and mild grades. The proposed DRDS is responsible for preventing vision loss of diabetic Type-II patients by accurate and potential detection achieved through the utilization of IoT architecture.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The performance of the proposed scheme with the benchmarked approaches of the literature is implemented using MATLAB R2010a. The complete evaluations of the proposed scheme are conducted using HRF, REVIEW, STARE and DRIVE data sets with subjective quantification provided by the experts for the purpose of potential retinal blood vessel segmentation.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijpcc-08-2020-0109","type":"journal-article","created":{"date-parts":[[2021,2,13]],"date-time":"2021-02-13T07:56:51Z","timestamp":1613203011000},"page":"220-236","source":"Crossref","is-referenced-by-count":3,"title":["Reliable IoT-based Health-care System for Diabetic Retinopathy Diagnosis to defend the Vision of Patients"],"prefix":"10.1108","volume":"17","author":[{"given":"Sengathir","family":"Janakiraman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deva Priya","family":"M.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christy Jeba Malar","family":"A.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karthick","family":"S.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anitha Rajakumari","family":"P.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,2,7]]},"reference":[{"issue":"13","key":"key2023072412474370200_ref001","doi-asserted-by":"crossref","first-page":"5200","DOI":"10.1167\/iovs.16-19964","article-title":"Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning","volume":"57","year":"2016","journal-title":"Investigative Opthalmology and Visual Science"},{"key":"key2023072412474370200_ref002","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.compbiomed.2013.11.014","article-title":"Detection and classification of retinal lesions for grading of diabetic retinopathy","volume":"45","year":"2014","journal-title":"Computers in Biology and Medicine"},{"issue":"6","key":"key2023072412474370200_ref003","first-page":"71","article-title":"Sustainable diabetic retinopathy diagnosis system using iot","volume":"1","year":"2019","journal-title":"International Research Journal of Multidisciplinary Technovation"},{"key":"key2023072412474370200_ref004","first-page":"83","article-title":"Smartphone-based decision support system for elimination of pathology-free images in diabetic retinopathy screening","volume-title":"International Conference on IoT Technologies for HealthCare","year":"2016"},{"issue":"14","key":"key2023072412474370200_ref005","article-title":"Diabetic retinopathy: current understanding, mechanisms, and treatment strategies","volume":"2","year":"2017","journal-title":"JCI Insight"},{"issue":"1","key":"key2023072412474370200_ref006","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s10916-010-9454-7","article-title":"Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review","volume":"36","year":"2012","journal-title":"Journal of Medical Systems"},{"issue":"2","key":"key2023072412474370200_ref007","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.bbe.2014.01.004","article-title":"Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images","volume":"34","year":"2014","journal-title":"Biocybernetics and Biomedical Engineering"},{"journal-title":"Sustainable Computing: Informatics and Systems","article-title":"IOT based sustainable diabetic retinopathy diagnosis system","year":"2018","key":"key2023072412474370200_ref008"},{"issue":"6","key":"key2023072412474370200_ref009","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s10916-019-1313-6","article-title":"Distinguishing proof of diabetic retinopathy detection by hybrid approaches in two dimensional retinal fundus images","volume":"43","year":"2019","journal-title":"Journal of Medical Systems"},{"year":"2020","key":"key2023072412474370200_ref010","article-title":"Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. 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