Ear Disease Detection Using R-CNN

EasyChair Preprint 7869

Abstract

Ear is the essential organ in human body. Due to lifestyle changes usage of headphone, speaker and Bluetooth speakers the ear drum gets affected and leads to loss of hearing. Early finds of the ear disease may rectify the problem. This project helps to identify the ear disease in image processing method.

Early detection of ear disease can easily solve the ear disease with the medical treatment. Due to improper life balance and low diagnosis with an accuracy a new diagnosis method can adopted. This research views the machine learning approach with the large amount inner ear images to perfectly identify the clinical cases of ear disease. This R-CNN algorithm is the new version of the CNN algorithm which convolutes the images into 20,542 parts of convolutional layers. There are eight types of ear disease found in this research. This disease may affect the roots of ear drum accumulation of wax and other common ear disease problem. The disease includes otitis externa, Wax Ear, Glue Ear, Otomycosis etc. The ear disease may have affected other parts of human body. It may affect the sensory roots of brain also to have the serious sensory roots issues.

Keyphrases: Deployment, Naive Bayes Classification, R-CNN, Trainset

@booklet{EasyChair:7869,
  author    = {S Anandamurugan and M Saravana Kumar and E G Prashanth and K Nithin},
  title     = {Ear Disease Detection Using R-CNN},
  howpublished = {EasyChair Preprint 7869},
  year      = {EasyChair, 2022}}