Automated Detection of Abnormalities in Chest X-Ray Images Using Convolutional Neural Networks

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2018 by IJPTT Journal
	Volume-8 Issue-2
	Year of Publication : 2018
	Authors : K. Aravinth Raaj, B. Joel Sherwin, S. Sabeetha Saraswathi
	DOI :  10.14445/22492615/IJPTT-V8I2P404

Citation

K. Aravinth Raaj, B. Joel Sherwin, S. Sabeetha Saraswathi "Automated Detection of Abnormalities in Chest X-Ray Images Using Convolutional Neural Networks". International Journal of P2P Network Trends and Technology (IJPTT).V8 (2) 18-24 March to April 2018. ISSN:2249-2615. www.ijpttjournal.org. Published by Seventh Sense Research Group.

Abstract

Heart and lung diseases account for 11% of total deaths in India, with the cumulative count of all chest related deaths accounting for almost 30% of the world`s mortality rate. A computer-aided diagnosis system is proposed that utilizes Convolutional Neural Networks (CNN) to classify images of chest x-rays into normal or abnormal labels, and if abnormal, attempts to identify the kind of abnormality/disease further. We trained Convolutional Neural Network (CNN) to classify tuberculosis, pneumonia, and cardiovascular abnormalities. The pre-trained neural network GoogLeNet is tuned using a dataset of labeled x-rays, and the model is used to classify an x-ray without prior knowledge in medicine. We extend our work further to classify all 11 diseases.

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Keywords
neural networks; deep learning; medical imaging; computer-aided diagnosis.