Chest X-ray Image Classification for Tuberculosis using Deep Convolutional Neural Network
Author(s):
Aditya Nair, Akshay Mohan, Jith James Fernandez, Sabin Biju Seema, Sreena V G
Keywords:
Tuberculosis classification, Chest X-Ray, Convolutional Neural network, Deep learning.
Abstract
Lung infections are severe health conditions that seriously affect the life of human, especially those like Tuberculosis (TB) which accounts for most of deaths yearly. Therefore, proper diagnosis of TB is very much essential. Chest X-Rays (CXR) are mostly been used by medical industry for detecting tuberculosis as well as other lung diseases. Deep learning based disease identification systems are in use nowadays, one among such a deep learning architecture is Convolutional Neural Network (CNN). In this work, a prediction model using Deep Convolutional Neural Networks is proposed for detecting TB from input Chest X-ray image. Evaluation of model performance was done using confusion matrix and accuracy as metrics. The developed custom network is paired with adam optimizer, which was able to precisely differentiate between normal and TB infected CXRs. Finally an interactive web application was built to deploy the model so that it can be used to make predictions on Chest X-rays as input.
Article Details
Unique Paper ID: 154190

Publication Volume & Issue: Volume 8, Issue 7

Page(s): 96 - 103
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