Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras
This paper compares and explores the performance of both mobile device camera and laptop camera as convenient tool for capturing images for noninvasive detection of Diabetes Mellitus (DM) using facial block texture features. Participants within age bracket 20 to 79 years old were chosen for the data...
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Archīum Ateneo
2023
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ph-ateneo-arc.discs-faculty-pubs-14202024-06-14T07:26:59Z Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras Garcia, Christina Abu, Patricia Angela R Reyes, Rosula SJ This paper compares and explores the performance of both mobile device camera and laptop camera as convenient tool for capturing images for noninvasive detection of Diabetes Mellitus (DM) using facial block texture features. Participants within age bracket 20 to 79 years old were chosen for the dataset. 12mp and 7mp mobile cameras, and a laptop camera were used to take the photo under normal lighting condition. Extracted facial blocks were classified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). 100 images were captured, preprocessed, filtered using Gabor, and iterated. Performance of the system was measured in terms of accuracy, specificity, and sensitivity. Best performance of 96.7% accuracy, 100% sensitivity, and 93% specificity were achieved from 12mp back camera using SVM with 100 images. 2023-01-01T08:00:00Z text https://archium.ateneo.edu/discs-faculty-pubs/419 https://doi.org/10.48550/arXiv.2307.15480 Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo Computer Vision and Pattern Recognition Artificial Intelligence Artificial Intelligence and Robotics Data Science Signal Processing |
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Computer Vision and Pattern Recognition Artificial Intelligence Artificial Intelligence and Robotics Data Science Signal Processing Garcia, Christina Abu, Patricia Angela R Reyes, Rosula SJ Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras |
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This paper compares and explores the performance of both mobile device camera and laptop camera as convenient tool for capturing images for noninvasive detection of Diabetes Mellitus (DM) using facial block texture features. Participants within age bracket 20 to 79 years old were chosen for the dataset. 12mp and 7mp mobile cameras, and a laptop camera were used to take the photo under normal lighting condition. Extracted facial blocks were classified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). 100 images were captured, preprocessed, filtered using Gabor, and iterated. Performance of the system was measured in terms of accuracy, specificity, and sensitivity. Best performance of 96.7% accuracy, 100% sensitivity, and 93% specificity were achieved from 12mp back camera using SVM with 100 images. |
format |
text |
author |
Garcia, Christina Abu, Patricia Angela R Reyes, Rosula SJ |
author_facet |
Garcia, Christina Abu, Patricia Angela R Reyes, Rosula SJ |
author_sort |
Garcia, Christina |
title |
Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras |
title_short |
Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras |
title_full |
Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras |
title_fullStr |
Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras |
title_full_unstemmed |
Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras |
title_sort |
non-invasive diabetes detection using gabor filter: a comparative analysis of different cameras |
publisher |
Archīum Ateneo |
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2023 |
url |
https://archium.ateneo.edu/discs-faculty-pubs/419 https://doi.org/10.48550/arXiv.2307.15480 |
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1803354317806108672 |