A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images
- Title
- A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images
- Creator
- Raju N.; Augustine D.P.; Anita H.B.
- Description
- Interstitial lung diseases (ILDs) are defined as a group of lung diseases that affect the interstitium and cause death among humans worldwide. It is more serious in underdeveloped countries as it is hard to diagnose due to the absence of specialists. Detecting and classifying ILD is a challenging task and many research activities are still ongoing. High-resolution computed tomography (HRCT) images have essentially been utilized in the diagnosis of this disease. Examining HRCT images is a difficult task, even for an experienced doctor. Information Technology, especially Artificial Intelligence, has started contributing to the accurate diagnosis of ILD from HRCT images. Similar patterns of different categories of ILD confuse doctors in making quick decisions. Recent studies have shown that corona patients with ILD also go on to sudden death. Therefore, the diagnosis of ILD is more critical today. Different deep learning approaches have positively impacted various image classification problems recently. The main objective of this proposed research work was to develop a deep learning model to classify the ILD categories from HRCT images. This proposed work aims to perform binary and multi-label classification of ILD using HRCT images on a customized VGG architecture. The proposed model achieved a high test accuracy of 95.18% on untrained data. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
- Source
- SN Computer Science, Vol-4, No. 2
- Date
- 2023-01-01
- Publisher
- Springer
- Subject
- Binary classification; Deep learning; HRCT; ILD; Multi-label classification
- 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; Anita H.B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 2662995X
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Raju N.; Augustine D.P.; Anita H.B., “A Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/14390.