Abstract
The increasing demand for non-invasive, rapid, and cost-effective disease diagnostics has driven advancements in integrating Iridology with computer vision and Artificial Intelligence (AI). This review examines research conducted from 2009 to 2024 on iris-based disease detection. The key findings showed the significant role of Machine Learning (ML) and Deep Learning (DL) in enhancing diagnostic accuracy and efficiency. Iridology-based intelligent systems show great promise for early detection of hidden diseases and organ dysfunctions, offering transformative potential for healthcare.
Conclusion
Research in complementary medical diagnostics, particularly in iris-based disease detection, has demonstrated the potential of leveraging image processing, computer vision, and artificial intelligence. Recent trends emphasize the increasing adoption of machine learning and deep learning techniques, often used in combination, due to their effectiveness in analyzing medical images. These approaches not only increase diagnostic accuracy but also reduce training time and enhance interface responsiveness.Preprocessing of iris images prior to AI-based classification is a critical step in improving system efficiency. Challenges, however, remain, especially in the areas of iris segmentation and auto-cropping, often arising from variations in lighting and image capture conditions.
Authors: Alaa Abdulkareem Ahmed, Mohammad Tariq Yaseen, Department of Electrical Engineering, College of Engineering,University of Mosul, Mosul,Iraq
Full Abstract: https://iridology-research.com/pdf/Recent+Advances+in+Iridology+based+Disease+Detection.pdf
