ArticleApplied intelligence (Dordrecht, Netherlands)2023
Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification.
Article in Applied intelligence (Dordrecht, Netherlands), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 45 citations in OpenAlex.
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- RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.PloS one · 2025Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images.PeerJ. Computer science · 2024Article
- COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI).Journal of clinical medicine · 2023Article
- Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.Diagnostics (Basel, Switzerland) · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- Detecting anteriorly displaced temporomandibular joint discs using super-resolution magnetic resonance imaging: a multi-center study.Frontiers in physiology · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has become a pandemic for the entire world, and it has significantly affected the world economy. The importance of early detection and treatment of the infection cannot be overstated. The traditional diagnosis techniques take more time in detecting the infection. Although, numerous deep learning-based automated solutions have recently been developed in this regard, nevertheless, the limitation of computational and battery power in resource-constrained devices makes it difficult to deploy trained models for real-time inference. In this paper, to detect the presence of COVID-19 in CT-scan images, an important weights-only transfer learning method has been proposed for devices with limited runt-time resources. In the proposed method, the pre-trained models are made point-of-care devices friendly by pruning less important weight parameters of the model. The experiments were performed on two popular VGG16 and ResNet34 models and the empirical results showed that pruned ResNet34 model achieved 95.47% accuracy, 0.9216 sensitivity, 0.9567 F-score, and 0.9942 specificity with 41.96% fewer FLOPs and 20.64% fewer weight parameters on the SARS-CoV-2 CT-scan dataset. The results of our experiments showed that the proposed method significantly reduces the run-time resource requirements of the computationally intensive models and makes them ready to be utilized on the point-of-care devices.
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