Non-invasive Diabetes Detection using Gabor Filter: A Comparative Analysis of Different Cameras

TL;DR

This study evaluates the effectiveness of using facial texture analysis via Gabor filters on images captured by different cameras for non-invasive diabetes detection, achieving high accuracy with mobile and laptop cameras.

Contribution

It compares camera types and machine learning classifiers for non-invasive diabetes detection using facial features, highlighting the optimal camera and method combination.

Findings

12mp back camera with SVM achieved 96.7% accuracy.

Facial block texture features can effectively indicate diabetes.

Mobile cameras can be viable tools for non-invasive diabetes screening.

Abstract

This paper compares and explores the performance of both mobile device camera and laptop camera as convenient tool for capturing images for non-invasive 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.