Deep Convolutional Neural Networks for Classification of Interstitial Lung Disease

Vardhan, Harsha Satya; Dash, Jatindra Kumar; Mohanty, Sachinandan

doi:10.2139/ssrn.3568854

Download This Paper

Open PDF in Browser

Add Paper to My Library

Deep Convolutional Neural Networks for Classification of Interstitial Lung Disease

Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020

8 Pages Posted: 7 Apr 2020

See all articles by Harsha Satya Vardhan

Sachinandan Mohanty

ICFAI Foundation for Higher Education (IFHE)

Date Written: April 5, 2020

Abstract

Automated lung tissue characterization of Interstitial Lung Disease is one of the most important aspects of the Computer Aided Disease diagnosis system. The problem remains challenging, even though there has been much research in this area. While deep learning has produced brilliant success in image applications over the past few years, the majority of training is with sub-optimal parameters, requiring unnecessary long training time, setting up hyper parameters. In this paper, we explore the classification of lung tissue pattern affected with interstitial lung disease (ILD) in high resolution computed tomography (HRCT) scans and evaluated different CNN architectures with and without transfer learning. The effect of cyclical learning rates, the hyper-parameters tuning and data augmentation on classification performance are studied using a popular publicly available dataset called MedGift dataset.

Keywords: Interstitial Lung Disease, Deep learning, cyclical learning rate, Hyper-parameters, Computed Tomography

Suggested Citation: Suggested Citation

Vardhan, Harsha Satya and Dash, Jatindra Kumar and Mohanty, Sachinandan, Deep Convolutional Neural Networks for Classification of Interstitial Lung Disease (April 5, 2020). Proceedings of the International Conference on Innovative Computing & Communications (ICICC) 2020, Available at SSRN: https://ssrn.com/abstract=3568854 or http://dx.doi.org/10.2139/ssrn.3568854