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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Main Authors: Garcia, Christina, Abu, Patricia Angela R, Reyes, Rosula SJ
Format: text
Published: Archīum Ateneo 2023
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Online Access:https://archium.ateneo.edu/discs-faculty-pubs/419
https://doi.org/10.48550/arXiv.2307.15480
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Institution: Ateneo De Manila University
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spelling 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
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Computer Vision and Pattern Recognition
Artificial Intelligence
Artificial Intelligence and Robotics
Data Science
Signal Processing
spellingShingle 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
description 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
publishDate 2023
url https://archium.ateneo.edu/discs-faculty-pubs/419
https://doi.org/10.48550/arXiv.2307.15480
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