Abstract:
Iris diagnosis based on the sophisticated patterns and properties of the human iris has appeared
as a prospective method for use in medicine, particularly in ophthalmology and biometrics. Deep learning
methodologies have gained substantial attention over recent years due to their ability to automate and
enhance iris diagnosis at high accuracy and reliability. This critical analysis discusses the different deep
learning architectures and techniques employed in iris diagnosis, with primary emphasis on convolutional
neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models integrating traditional
image processing techniques with machine learning algorithms. We discuss key challenges encountered
in iris recognition, including changes in lighting, image quality, and occlusions, and how deep learning
technologies counter these challenges. The review also includes different deep learning data sets,
performance measures, and performance analysis of existing models. We also discuss the clinical
application of iris diagnosis, specifically its application in the detection of disease, e.g., glaucoma, diabetic
retinopathy, kidney disease and other eye diseases. We finally offer potential future directions within the
field, including the requirement for more robust models, real-time diagnosis, and compatibility with
wearable technology. This review is intended to offer a comprehensive summary of the present status of
deep learning technologies within iris diagnosis and their potential in future medical advancement.
Authors: Baba Fakruddin Ali B H1, Sai Kiran Sharma2, Shashi kanth Gupta
Assistant professor, Department of CSE, Nagarjuna College of Engineering Bangalore
Full Abstract: https://iridology-research.com/pdf/Mannuscript_Baba_F_Ali.pdf
