mlsanityData to 7 Oct 2026

COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs

arXiv 2209.10963 ↗First posted 22 Sep 2022eess.IV cs.CVcited by 12

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Abstract (arXiv)

In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast variation, and high structural similarity between infection and background. In this regard, a new two-phase deep convolutional neural network (CNN) based diagnostic system is proposed to detect minute irregularities and analyze COVID-19 infection. In the first phase, a novel SB-STM-BRNet CNN is developed, incorporating a new channel Squeezed and Boosted (SB) and dilated convolutional-based Split-Transform-Merge (STM) block to detect COVID-19 infected lung CT images. The new STM blocks performed multi-path region-smoothing and boundary operations, which helped to learn minor contrast variation and global COVID-19 specific patterns. Furthermore, the diverse boosted channels are achieved using the SB and Transfer Learning concepts in STM blocks to learn texture variation between COVID-19-specific and healthy images. In the second phase, COVID-19 infected images are provided to the novel COVID-CB-RESeg segmentation CNN to identify and analyze COVID-19 infectious regions. The proposed COVID-CB-RESeg methodically employed region-homogeneity and heterogeneity operations in each encoder-decoder block and boosted-decoder using auxiliary channels to simultaneously learn the low illumination and boundaries of the COVID-19 infected region. The proposed diagnostic system yields good performance in terms of accuracy: 98.21 %, F-score: 98.24%, Dice Similarity: 96.40 %, and IOU: 98.85 % for the COVID-19 infected region. The proposed diagnostic system would reduce the burden and strengthen the radiologist's decision for a fast and accurate COVID-19 diagnosis.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 24 of its 64 references that are in the map. First posted on arXiv 22 Sep 2022.

Builds on

24 of 64 references.

References · 24

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12 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 2; 2025: 3; 2026: 1.

12 papers.

  1. 2022Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNNcited by 7
  2. 2022A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detectioncited by 6
  3. 2023COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Imagescited by 4
  4. 2025A Novel Channel Boosted Residual CNN-Transformer with Regional-Boundary Learning for Breast Cancer Detectioncited by 3
  5. 2025CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual, Spatial, and Boundary-Aware Learning for Brain Tumor MRI Analysiscited by 3
  6. 2025RS-CA-HSICT: A Residual and Spatial Channel Augmented CNN Transformer Framework for Monkeypox Detectioncited by 3
  7. 2024A Novel Feature Map Enhancement Technique Integrating Residual CNN and Transformer for Alzheimer Diseases Diagnosiscited by 2
  8. 2022Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Frameworkcited by 1
  9. 2023AutoLungDx: A Hybrid Deep Learning Approach for Early Lung Cancer Diagnosis Using 3D Res-U-Net, YOLOv5, and Vision Transformerscited by 1
  10. 2026RSwinV2-MD: An Enhanced Residual SwinV2 Transformer for Monkeypox Detection from Skin Imagescited by 1
  11. 2023Drug Repurposing Targeting COVID-19 3CL Protease using Molecular Docking and Machine Learning Regression Approachcited by 0
  12. 2024A Novel Decision Ensemble Framework: Customized Attention-BiLSTM and XGBoost for Speculative Stock Price Forecastingcited by 0