ArticleComputers in biology and medicine2022
A dual-stage deep convolutional neural network for automatic diagnosis of COVID-19 and pneumonia from chest CT images.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 24 citations in OpenAlex.
- Article
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data.Health information science and systems · 2024Article
- A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images.Heliyon · 2024Article
- 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
- ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.Computers in biology and medicine · 2023Article
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
Corrections and comments
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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
In the Coronavirus disease-2019 (COVID-19) pandemic, for fast and accurate diagnosis of a large number of patients, besides traditional methods, automated diagnostic tools are now extremely required. In this paper, a deep convolutional neural network (CNN) based scheme is proposed for automated accurate diagnosis of COVID-19 from lung computed tomography (CT) scan images. First, for the automated segmentation of lung regions in a chest CT scan, a modified CNN architecture, namely SKICU-Net is proposed by incorporating additional skip interconnections in the U-Net model that overcome the loss of information in dimension scaling. Next, an agglomerative hierarchical clustering is deployed to eliminate the CT slices without significant information. Finally, for effective feature extraction and diagnosis of COVID-19 and pneumonia from the segmented lung slices, a modified DenseNet architecture, namely P-DenseCOVNet is designed where parallel convolutional paths are introduced on top of the conventional DenseNet model for getting better performance through overcoming the loss of positional arguments. Outstanding performances have been achieved with an F
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.