A novel approach to classify pharyngitis using potential features based on doctor’s perspective

Abstract

Pharyngitis is an inflammation of the oropharynx’s mucous membranes. It is typically brought on by a bacterial illness. The outburst of latest technologies has created the need for remote care of detecting diseases like pharyngitis through images of throat taken with help of smart camera. In recent years, research has forwarded with help of deep learning in classifying pharyngitis. But deep learning models require at least one hour training and requires considerably large data set to get a good accuracy. In this paper, we focused on this time constraint and are proposing a novel approach PFDP to classify pharyngitis through detection of potential features based on doctor’s perspective. We have extracted the tiny portions of image which the doctor observes them as infected and calculated frequencies of the occurrences of these portions and are given to custom made decision rules. The classification results showed significant improvement in performance in terms of time taken to reach average accuracy of 70%. It has taken only 5 minutes to extract counts of infected patterns and 1 more minute to get classification results by decision rules of if-then-else rules. We have conducted the experiment on set of 800 images. Though accuracy is lesser than that of what other works achieved but time taken to extract features is significantly lower than that of previous works. Also our approach does not require training and can be applied where scarcity of dataset exists. We assure that our approach is a new direction of research and can compete with more state of the art works in future.