Abstract Objective: To develop an automated system to classify extra ocular diseases Study Design: Retrospective cohort Method: The entire dataset consists of about 7,244 labelled images of patients from Drashti Netralaya Eye Hospital in Gujarat, India. Five diseases were selected for classification: Corneal scars, Dermoid Cyst, Strabismus, Ptosis, and Ocular Surface Disease. Histogram of Oriented Gradient feature descriptors were utilized with Support Vector Machines and Logistic Regression. Modern Neural Network architectures were also applied.
Bottleneck CNN and Logistic Regression (Balanced) both performed well according to different error measurements. This work outlines the development of a classifier for extra ocular conditions that uses natural, noisy images of faces taken with point-and-shoot cameras. Outcome Measures: Accuracy of diagnosis Result: The Bottleneck CNN achieved…
