Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region
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. Ma...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2023/9931010 |
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| author | L. Aneesh Euprazia A. Rajeswari K. K. Thyagharajan N. R. Shanker |
| author_facet | L. Aneesh Euprazia A. Rajeswari K. K. Thyagharajan N. R. Shanker |
| author_sort | L. Aneesh Euprazia |
| collection | DOAJ |
| description | 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’s palsy, rubeosis faciei, scleroderma, and vitiligo were collected from existing face videos. Moreover, face images are collected from diabetic persons in India. Viola Jones’ face-detecting algorithm extracts face skin regions from a diabetic person’s face image in video frames. The affected skin area on the diabetic person’s 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. |
| format | Article |
| id | doaj-art-6a652b5226d948f18caa060c1638d81f |
| institution | Kabale University |
| issn | 2314-6753 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-6a652b5226d948f18caa060c1638d81f2025-02-03T06:42:46ZengWileyJournal of Diabetes Research2314-67532023-01-01202310.1155/2023/9931010Type 1 and Type 2 Diabetes Measurement Using Human Face Skin RegionL. Aneesh Euprazia0A. Rajeswari1K. K. Thyagharajan2N. R. Shanker3Computer Science and EngineeringComputer Science and EngineeringElectronics and Communication EngineeringDepartment of Computer Science and EngineeringAim. 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’s palsy, rubeosis faciei, scleroderma, and vitiligo were collected from existing face videos. Moreover, face images are collected from diabetic persons in India. Viola Jones’ face-detecting algorithm extracts face skin regions from a diabetic person’s face image in video frames. The affected skin area on the diabetic person’s 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.http://dx.doi.org/10.1155/2023/9931010 |
| spellingShingle | L. Aneesh Euprazia A. Rajeswari K. K. Thyagharajan N. R. Shanker Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region Journal of Diabetes Research |
| title | Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region |
| title_full | Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region |
| title_fullStr | Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region |
| title_full_unstemmed | Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region |
| title_short | Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region |
| title_sort | type 1 and type 2 diabetes measurement using human face skin region |
| url | http://dx.doi.org/10.1155/2023/9931010 |
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