An Eye Ailment Appreciation Scheme from Bacterial Infective Diseases Using Machine Learning Techniques

Abstract

 Infectious keratitis is the most common entities of corneal diseases, in which pathogen produce in the cornea leading to softness  and destroying of the corneal tissues. Infectious keratitis is a medical emergency, for which a fast and accurate diagnosis is needed fortroy speedy initiation of prompt and simple treatment to cure  the disease progress and to reduce the extent of corneal damage; otherwise it may develop sight-threatening and even is eye-globe-threatening condition.  In this paper, infectious corneal disease is the classification of clinical images. The assinged system automatically divides the countance components from the frontal facial image and extracts the eye part. The proposed method analyzes and classifies peri-orbital cellulitis, and Bitot’s spot of vitamin A deficiency. From the experimental results, we see that the DCNNmodel outperforms SVM models. We also compare our method with some other existing methods. Our process shows improved accuracy compared to other process. The average accuracy rate of our DCNN model is 98.79% with sensitivity of 97% and specificity of 99%.