Interstitial Lung Disease Classification Using Feed Forward Neural Networks

DOI

10.2991/iccasp-16.2017.75How to use a DOI?

Keywords

ILD classification, second order statistics, Feed Forward Neural, Networks, SVM, K-NN

Abstract

This paper presents a classification scheme for Interstitial Lung Disease (ILD) pattern using Patch based approach and ANN classifier. Various methods are proposed for classification of ILD patterns but still accuracy can be improved when ILD cases are complex. In this paper, we have extracted features from (31×31) size patch using histogram of Local Binary Patterns (LBP) and second order statistics such as Grey level co-occurrence matrix (GLCM), Grey level run length matrix (GLRLM). A two layer Feed-Forward Neural Network trained with Scaled Conjugate Gradient Back-propagation algorithm is used for classification. Accuracy of the work carried out in this paper is 93.41 %. Results are verified and compared with different classifiers such as k-NN and SVM. This study has been carried out on publicly available database of ILD cases. ILD patches have been collected from 2-D Region of interest (ROI) marked by expert radiologist. Five frequently seen ILD patterns: Normal, Emphysema, Fibrosis, Ground Glass and Micronodule are considered in this study. Experimental results with proposed scheme outperforms in classification of ILD patterns.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - A. Dudhane
AU  - G. Shingadkar
AU  - P. Sanghavi
AU  - B. Jankharia
AU  - S. Talbar
PY  - 2016/12
DA  - 2016/12
TI  - Interstitial Lung Disease Classification Using Feed Forward Neural Networks
BT  - Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
PB  - Atlantis Press
SP  - 509
EP  - 515
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasp-16.2017.75
DO  - 10.2991/iccasp-16.2017.75
ID  - Dudhane2016/12
ER  -