Iris-Based Organ Disorder Detection Using Iridology Integrated with Advanced Deep Learning Techniques
Keywords:
Deep Learning, Convolutional Neural Networks,, Automated Diagnosis, Medical Imaging, Feature Extraction, AI in Healthcare, Non-Invasive DiagnosticsAbstract
The research develops an enhanced deep learning framework for iris-based organ disorder assessment which applies the concepts of iridology. Professional interpretation of iris patterns within traditional iridology generates inconsistent diagnosis results because of expert subjectivity. The presented research develops an independent deep learning model which analyzes iris imagery to detect possible organ illnesses. The developed system brings together convolutional neural networks (CNNs) which extract discriminative features from iris images before assigning them to health conditions of specific organs. A collection of high-resolution iris images comes with the necessary medical condition labels. The system needs extensive training as well as validation to reach diagnostic accuracy levels. The new approach yields better medical predictions than conventional iridology methods through reduced human involvement and better reliability. The study creates pathfinder technology for AI-based iridology which produces an automated diagnostic method that works at scale and without physical contact. Future progress will involve enlarging the dataset collection and improving model precision for active application in medical workflows
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