{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T03:29:00Z","timestamp":1772767740253,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Diabetes Research"],"published-print":{"date-parts":[[2023,9,26]]},"abstract":"<jats:p>Aim. Analyse the diabetes mellitus (DM) of a person through the facial skin region using vision diabetology. Diabetes mellitus is caused by persistent high blood glucose levels and related complications, which show variation in facial skin regions due to reduced blood flow in the facial arteries. Materials and Method. In this study, 200 facial images of diabetes patients with skin conditions such as Bell\u2019s palsy, rubeosis faciei, scleroderma, and vitiligo were collected from existing face videos. Moreover, face images are collected from diabetic persons in India. Viola Jones\u2019 face-detecting algorithm extracts face skin regions from a diabetic person\u2019s face image in video frames. The affected skin area on the diabetic person\u2019s face is detected using HSV colour model segmentation. The proposed multiwavelet transform convolutional neural network (MWTCNN) extracts the features for diabetic measurement from up- and downfacial scaled images of diabetic persons. Results. The existing deep learning models are compared with the proposed MWTCNN model, which provides the highest accuracy of 98.3%. Conclusion. The facial skin region-based diabetic measurement avoids pricking of the serum and is used for continuous glucose monitoring.<\/jats:p>","DOI":"10.1155\/2023\/9931010","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T23:50:09Z","timestamp":1695772209000},"page":"1-14","source":"Crossref","is-referenced-by-count":2,"title":["Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6505-8804","authenticated-orcid":true,"given":"L. 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