mlsanityData to 7 Oct 2026

COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images

arXiv 2302.02619 ↗First posted 6 Feb 2023eess.IV cs.CV cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

COVID-19 is a new pathogen that first appeared in the human population at the end of 2019, and it can lead to novel variants of pneumonia after infection. COVID-19 is a rapidly spreading infectious disease that infects humans faster. Therefore, efficient diagnostic systems may accurately identify infected patients and thus help control their spread. In this regard, a new two-stage analysis framework is developed to analyze minute irregularities of COVID-19 infection. A novel detection Convolutional Neural Network (CNN), STM-BRNet, is developed that incorporates the Split-Transform-Merge (STM) block and channel boosting (CB) to identify COVID-19 infected CT slices in the first stage. Each STM block extracts boundary and region-smoothing-specific features for COVID-19 infection detection. Moreover, the various boosted channels are obtained by introducing the new CB and Transfer Learning (TL) concept in STM blocks to capture small illumination and texture variations of COVID-19-specific images. The COVID-19 CTs are provided with new SA-CB-BRSeg segmentation CNN for delineating infection in images in the second stage. SA-CB-BRSeg methodically utilized smoothening and heterogeneous operations in the encoder and decoder to capture simultaneously COVID-19 specific patterns that are region homogeneity, texture variation, and boundaries. Additionally, the new CB concept is introduced in the decoder of SA-CB-BRSeg by combining additional channels using TL to learn the low contrast region. The proposed STM-BRNet and SA-CB-BRSeg yield considerable achievement in accuracy: 98.01 %, Recall: 98.12%, F-score: 98.11%, and Dice Similarity: 96.396%, IOU: 98.845 % for the COVID-19 infectious region, respectively. The proposed two-stage framework significantly increased performance compared to single-phase and other reported systems and reduced the burden on the radiologists.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 61 references that are in the map. First posted on arXiv 6 Feb 2023.

Builds on

23 of 61 references.

References · 23

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  4. 2020Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Imagescited by 184
  5. 2020Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentationcited by 53
  6. 2020JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentationcited by 52
  7. 2020Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysiscited by 38
  8. 2020Lung Infection Quantification of COVID-19 in CT Images with Deep Learningcited by 28
  9. 2020Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Imagescited by 26
  10. 2019MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learningcited by 18
  11. 2020COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNNcited by 15
  12. 2021Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encodercited by 15
11 more
  1. 2022A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicroncited by 13
  2. 2022COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNscited by 12
  3. 2018U-SegNet: Fully Convolutional Neural Network based Automated Brain tissue segmentation Toolcited by 11
  4. 2020Classification and Region Analysis of COVID-19 Infection using Lung CT Images and Deep Convolutional Neural Networkscited by 11
  5. 2022A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRIcited by 10
  6. 2022IoT Malware Detection Architecture using a Novel Channel Boosted and Squeezed CNNcited by 10
  7. 2020An automatic COVID-19 CT segmentation network using spatial and channel attention mechanismcited by 8
  8. 2022Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNNcited by 7
  9. 2019Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industrycited by 6
  10. 2022A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detectioncited by 6
  11. 2020Harmony-Search and Otsu based System for Coronavirus Disease (COVID-19) Detection using Lung CT Scan Imagescited by 3

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3.

4 papers.

  1. 2024RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosiscited by 4
  2. 2024A Novel Feature Map Enhancement Technique Integrating Residual CNN and Transformer for Alzheimer Diseases Diagnosiscited by 2
  3. 2023AutoLungDx: A Hybrid Deep Learning Approach for Early Lung Cancer Diagnosis Using 3D Res-U-Net, YOLOv5, and Vision Transformerscited by 1
  4. 2024A Novel Decision Ensemble Framework: Customized Attention-BiLSTM and XGBoost for Speculative Stock Price Forecastingcited by 0