An efficient deep learning approach for identifying interstitial lung diseases using HRCT images
- Title
- An efficient deep learning approach for identifying interstitial lung diseases using HRCT images
- Creator
- Raju N.; Augustine D.P.
- Description
- Interstitial lung disease (ILD) encompasses over 200 fatal lung disorders affecting the interstitium, leading to significant mortality rates. We propose an AI-driven approach to diagnose and classify ILD from high-resolution computed tomography (HRCT) images. The research utilises a dataset of 3,045 HRCT images and employs a two-tier ensemble method that combines various machine learning (ML) models, convolutional neural networks (CNNs), and transfer learning. Initially, ML models achieve high accuracy, with the J48 model at 93.08% accuracy, mainly highlighting the importance of diagonal-wise standard deviation. Deep learning techniques are then applied, with three CNN models achieving test accuracies of 94.08%, 92.04%, and 93.72%. Transfer learning models also show promise, with InceptionV3 at 92.48% accuracy. Ensembling these models further boosts accuracy, with the ensemble of three CNN models reaching 97.42%. This research has the potential to advance ILD diagnosis, offering a robust computational framework that enhances accuracy and ultimately improves patient outcomes. Copyright 2024 Inderscience Enterprises Ltd.
- Source
- International Journal of Computational Science and Engineering, Vol-27, No. 3, pp. 286-301.
- Date
- 2024-01-01
- Publisher
- Inderscience Publishers
- Subject
- deep learning; DL; high-resolution computed tomography; HRCT; ILD; interstitial lung disease; multi-label classification; transfer learning
- Coverage
- Raju N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Augustine D.P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 17427185
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Raju N.; Augustine D.P., “An efficient deep learning approach for identifying interstitial lung diseases using HRCT images,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/13678.