Auto-diagnosis of covid-19 using lung ct images with semi-supervised shallow learning network
- Title
- Auto-diagnosis of covid-19 using lung ct images with semi-supervised shallow learning network
- Creator
- Konar D.; Panigrahi B.K.; Bhattacharyya S.; Dey N.; Jiang R.
- Description
- In the current world pandemic situation, the contagious Novel Coronavirus Disease 2019 (COVID-19) has raised a real threat to human lives owing to infection on lung cells and human respiratory systems. It is a daunting task for the researchers to find suitable infection patterns on lung CT images for automated diagnosis of COVID-19. A novel integrated semi-supervised shallow neural network framework comprising a Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) for automatic segmentation of lung CT images followed by Fully Connected (FC) layers, is proposed in this article. The proposed PQIS-Net model is aimed at providing fully automated segmentation of lung CT slices without incorporating pre-trained convolutional neural network based models. A parallel trinity of layered structure of quantum bits are interconnected using an N -connected second order neighborhood-based topology in the suggested PQIS-Net architecture for segmentation of lung CT slices with wide variations of local intensities. A random patch-based classification on PQIS-Net segmented slices is incorporated at the classification layers of the suggested semi-supervised shallow neural network framework. Intensive experiments have been conducted using three publicly available data sets, one for purely segmentation task and the other two for classification (COVID-19 diagnosis). The experimental outcome on segmentation of CT slices using self-supervised PQIS-Net and the diagnosis efficiency (Accuracy, Precision and AUC) of the integrated semi-supervised shallow framework is found to be promising. The proposed model is also found to be superior than the best state-of-the-art techniques and pre-trained convolutional neural network-based models, specially in COVID-19 and Mycoplasma Pneumonia (MP) screening. 2013 IEEE.
- Source
- IEEE Access, Vol-9, pp. 28716-28728.
- Date
- 2021-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- 3D-UNet; COVID-19; Lung CT image segmentation; QIS-Net; ResNet50
- Coverage
- Konar D., Department of Electrical Engineering, IIT Delhi, New Delhi, 110016, India, Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok, 737136, India; Panigrahi B.K., Department of Electrical Engineering, IIT Delhi, New Delhi, 110016, India; Bhattacharyya S., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Bengaluru, 560029, India; Dey N., Department of Computer Science and Engineering, JIS University, Kolkata, 700109, India; Jiang R., School of Computing and Communications, Lancaster University, Lancaster, LA1 4YW, United Kingdom
- Rights
- All Open Access; Gold Open Access; Green Open Access
- Relation
- ISSN: 21693536
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Konar D.; Panigrahi B.K.; Bhattacharyya S.; Dey N.; Jiang R., “Auto-diagnosis of covid-19 using lung ct images with semi-supervised shallow learning network,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 3, 2025, https://archives.christuniversity.in/items/show/16094.