Discriminative Feature Extraction from X-ray Images using Deep Convolutional Neural Networks

M, Srinivas and Roy, D and C, Krishna Mohan (2016) Discriminative Feature Extraction from X-ray Images using Deep Convolutional Neural Networks. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, MAR 20-25, 2016, Shanghai, PEOPLES R CHINA.

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Feature extraction is one of the most important phases of medical image classification which requires extensive domain knowledge. Convolutional Neural Networks (CNN) have been successfully used for feature extraction in images from different domains involving a lot of classes. In this paper, CNNs are exploited to extract a hierarchical and discriminative representation of X-ray images. This representation is then used for classification of the X-ray images as various parts of the body. Visualization of the feature maps in the hidden layers show that features learnt by the CNN resemble the essential features which help discern the discrimination among different body parts. A comparison on the standard IRMA X-ray image dataset demonstrates that the CNNs easily outperform classifiers with hand-engineered features.

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IITH Creators:
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Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Convolutional Neural Networks (CNN); X-ray image; Feature Extraction
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 05 Jan 2017 06:13
Last Modified: 01 Sep 2017 09:15
URI: http://raiith.iith.ac.in/id/eprint/2972
Publisher URL: https://doi.org/10.1109/ICASSP.2016.7471809
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