ArticleMultimedia tools and applications2023
Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans.
Article in Multimedia tools and applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An Improved Capsule Network for Image Classification Using Multi-Scale Feature Extraction.Journal of imaging · 2025Article
- Transferable deep learning with coati optimization algorithm based mitotic nuclei segmentation and classification model.Scientific reports · 2024Article
- Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs.Scientific reports · 2024Article
- 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
- COVID-19 Diagnosis in Computerized Tomography (CT) and X-ray Scans Using Capsule Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus, a virus that spread worldwide rapidly and was eventually declared a pandemic. The rapid spread made it essential to detect Coronavirus infected people to control the further spread. Recent studies show that radiological images such as X-Rays and CT scans provide essential information in detecting infection using deep learning models. This paper proposes a shallow architecture based on Capsule Networks with convolutional layers to detect COVID-19 infected persons. The proposed method combines the ability of the capsule network to understand spatial information with convolutional layers for efficient feature extraction. Due to the model's shallow architecture, it has 23
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Registered trials
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