ArticleEngineering applications of artificial intelligence2023
Prognosticating various acute covid lung disorders from COVID-19 patient using chest CT Images.
Article in Engineering applications of artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- Few-shot learning for the classification of colorectal neuroendocrine tumors and polyps on endoscopic images.The Journal of international medical research · 2025Article
- Hematologic and Computed Tomography Features for Discriminating COVID-19 From Non-COVID-19 Pneumonia.Archives of Razi Institute · 2025Article
- Classification of Severity of Lung Parenchyma Using Saliency and Discrete Cosine Transform Energy in Computed Tomography of Patients With COVID-19.International journal of telemedicine and applications · 2025Article
- Few-shot learning to identify atypical endometrial hyperplasia and endometrial cancer based on transvaginal ultrasonic images.Heliyon · 2024Article
- Detecting Internal Defects in FRP-Reinforced Concrete Structures through the Integration of Infrared Thermography and Deep Learning.Materials (Basel, Switzerland) · 2024Article
- Article
- A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images.Journal of digital imaging · 2023Article
- Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.Frontiers in cellular and infection microbiology · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The global spread of coronavirus illness has surged dramatically, resulting in a catastrophic pandemic situation. Despite this, accurate screening remains a significant challenge due to difficulties in categorizing infection regions and the minuscule difference between typical pneumonia and COVID (Coronavirus Disease) pneumonia. Diagnosing COVID-19 using the Mask Regional-Convolutional Neural Network (Mask R-CNN) is proposed to classify the chest computerized tomographic (CT) images into COVID-positive and COVID-negative. Covid-19 has a direct effect on the lungs, causing damage to the alveoli, which leads to various lung complications. By fusing multi-class data, the severity level of the patients can be classified using the meta-learning few-shot learning technique with the residual network with 50 layers deep (ResNet-50) as the base classifier. It has been tested with the outcome of COVID positive chest CT image data. From these various classes, it is possible to predict the onset possibilities of acute COVID lung disorders such as sepsis, acute respiratory distress syndrome (ARDS), COVID pneumonia, COVID bronchitis, etc. The first method of classification is proposed to diagnose whether the patient is affected by COVID-19 or not; it achieves a mean Average Precision (mAP) of
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