Iridology based human health conditions predictions with computer vision and deep learning

Abstract

In today’s world, ocular diseases have become widespread. The “ocular illness” refers to any abnormality, impairment, or dysfunction of vision that impacts the eye. The most common forms of ocular disease include dry eyes, conjunctivitis, blepharitis, and glaucoma. Each of these conditions can cause redness, pain, and blurry vision. To resolve the challenges in human ocular diseases, the need for detection becomes an important part of every person’s life. There are numerous approaches by various researchers to detect and predict the disease early, but the previous approaches required certain improvements to predict the ocular disease more accurately and easily. In the proposed method, deep learning-based ocular disease prediction with high accuracy and low cost is implemented. The framework deals with the collection of data from the diabetes-iridology dataset and pre-processing using the Contrast limited adaptive histogram equalization (CLAHE). The Attenuation-based U-net is the most advanced proposed method used for the segmentation of ocular disease with higher accuracy. The Adaptive Residual Squeeze and Excitation network (ARSEnet) is the proposed method used for a better classification process. Based on the evaluation results, the proposed technique better classified eye illness. The Python tool is used to carry out the recommended method. The proposed strategy outperformed others in terms of accuracy, precision, specificity, recall, and f1 score. The accuracy rate of the proposed method is 97.02%. The resultant analysis clearly proves that the proposed system provides the best accuracy for predicting ocular disease.