Deep Learning-Based Noninvasive Blood Glucose Estimation
Estimating blood glucose levels (BGLs) noninvasively is a rapidly advancing field driven by the need for effective and painless glucose monitoring solutions for diabetic patients. This study explores deep learning (DL) models applied to noninvasive techniques for accurate BGL estimation. Thermal ima...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/je/1134023 |
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| author | Shatha M. Ali Younis M. Abbosh Dia M. Ali |
| author_facet | Shatha M. Ali Younis M. Abbosh Dia M. Ali |
| author_sort | Shatha M. Ali |
| collection | DOAJ |
| description | Estimating blood glucose levels (BGLs) noninvasively is a rapidly advancing field driven by the need for effective and painless glucose monitoring solutions for diabetic patients. This study explores deep learning (DL) models applied to noninvasive techniques for accurate BGL estimation. Thermal images were collected for Type I diabetes after confirming BGLs using a glucometer. DL techniques were then employed to classify the thermal images into three BGL classes (low, high, and normal). DarkNet and ShuffleNet convolutional neural networks (CNNs) are used to classify the thermal image and get the best performance, with an overall accuracy of 98% for DarkNet and 100% for ShuffleNet CNN. |
| format | Article |
| id | doaj-art-1a582d2a4c7045cd8679f8d45cb2aacc |
| institution | OA Journals |
| issn | 2314-4912 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-1a582d2a4c7045cd8679f8d45cb2aacc2025-08-20T02:27:19ZengWileyJournal of Engineering2314-49122025-01-01202510.1155/je/1134023Deep Learning-Based Noninvasive Blood Glucose EstimationShatha M. Ali0Younis M. Abbosh1Dia M. Ali2Communication EngineeringBiomedical EngineeringBiomedical EngineeringEstimating blood glucose levels (BGLs) noninvasively is a rapidly advancing field driven by the need for effective and painless glucose monitoring solutions for diabetic patients. This study explores deep learning (DL) models applied to noninvasive techniques for accurate BGL estimation. Thermal images were collected for Type I diabetes after confirming BGLs using a glucometer. DL techniques were then employed to classify the thermal images into three BGL classes (low, high, and normal). DarkNet and ShuffleNet convolutional neural networks (CNNs) are used to classify the thermal image and get the best performance, with an overall accuracy of 98% for DarkNet and 100% for ShuffleNet CNN.http://dx.doi.org/10.1155/je/1134023 |
| spellingShingle | Shatha M. Ali Younis M. Abbosh Dia M. Ali Deep Learning-Based Noninvasive Blood Glucose Estimation Journal of Engineering |
| title | Deep Learning-Based Noninvasive Blood Glucose Estimation |
| title_full | Deep Learning-Based Noninvasive Blood Glucose Estimation |
| title_fullStr | Deep Learning-Based Noninvasive Blood Glucose Estimation |
| title_full_unstemmed | Deep Learning-Based Noninvasive Blood Glucose Estimation |
| title_short | Deep Learning-Based Noninvasive Blood Glucose Estimation |
| title_sort | deep learning based noninvasive blood glucose estimation |
| url | http://dx.doi.org/10.1155/je/1134023 |
| work_keys_str_mv | AT shathamali deeplearningbasednoninvasivebloodglucoseestimation AT younismabbosh deeplearningbasednoninvasivebloodglucoseestimation AT diamali deeplearningbasednoninvasivebloodglucoseestimation |