Interstitial Lung Disease Classification Using Feed Forward Neural Networks
- A. Dudhane, G. Shingadkar, P. Sanghavi, B. Jankharia, S. Talbar
- Corresponding Author
- A. Dudhane
Available Online December 2016.
- https://doi.org/10.2991/iccasp-16.2017.75How to use a DOI?
- ILD classification, second order statistics, Feed Forward Neural, Networks, SVM, K-NN
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - International Conference on Communication and Signal Processing 2016 (ICCASP 2016) PB - Atlantis Press UR - https://doi.org/10.2991/iccasp-16.2017.75 DO - https://doi.org/10.2991/iccasp-16.2017.75 ID - Dudhane2016/12 ER -