Deep Learning-Based Verification of Iridology in Diagnosing Type II Diabetes Mellitus
Abstract
Type II Diabetes Mellitus (Type II DM) is a chronic condition that has detrimental effect on vital organs if left untreated, necessitating early diagnosis and treatment. Iridology, a subset of Complementary and Alternative Medicine (CAM), has the potential to serve as a tool for noninvasive early diagnosis of Type II DM. Iridology involves analyzing the characteristics of iris such as color and pattern for detection of organ and system defects. Deep learning algorithm is one of the promising methods in diagnosing various health-related issues. In this study, we have demonstrated the efficiency of iridology in diagnosing Type II DM using deep learning algorithms. Near Infra-Red images of iris were captured using iris scanner from 178 voluntary subjects belonging to two categories namely, Type II DM (95 subjects) and nondiabetic or healthy category (83 subjects). We have developed an algorithm using Fully Convolutional Neural network for effective iris segmentation. Normalized iris images were used to crop out our region of interest, pancreas, based on the iridology chart. Classification networks such as AlexNet, VGG-16, and ResNet-50 were used to classify Type II DM versus healthy category. Our proposed model for iris segmentation achieved an accuracy and sensitivity of 0.99, specificity and F-Score of 0.98, and a precision of 0.97. Results obtained using AlexNet classifier exhibits better classification accuracy of 95.85% for Zero-padding based resized image. The classifier yielded a sensitivity, specificity, and precision of 95.80%, 95.85%, and 96.11%, respectively. Our study results establish the efficacy and emphasize the importance of the proposed algorithm for diagnosing Type II DM.
