Res-Dense Net for 3D Covid Chest CT-scan classification
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Abstract (arXiv)
One of the most contentious areas of research in Medical Image Preprocessing is 3D CT-scan. With the rapid spread of COVID-19, the function of CT-scan in properly and swiftly diagnosing the disease has become critical. It has a positive impact on infection prevention. There are many tasks to diagnose the illness through CT-scan images, include COVID-19. In this paper, we propose a method that using a Stacking Deep Neural Network to detect the Covid 19 through the series of 3D CT-scans images . In our method, we experiment with two backbones are DenseNet 121 and ResNet 101. This method achieves a competitive performance on some evaluation metrics
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 9 Aug 2022.
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References · 13
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- 2020Deep Transparent Prediction through Latent Representation Analysiscited by 72
- 2021MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysiscited by 72
- 2019Macro F1 and Macro F1cited by 70
- 2021COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer modelscited by 27
- 2020Diagnosing COVID-19 Pneumonia from X-Ray and CT Images using Deep Learning and Transfer Learning Algorithmscited by 24
- 2021A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Imagescited by 6
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- 2020Classification of COVID-19 in CT Scans using Multi-Source Transfer Learningcited by 3
- 2021COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DBcited by 3
- 2021TeliNet: Classifying CT scan images for COVID-19 diagnosiscited by 2
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