mlsanityData to 7 Oct 2026

Data Augmentation and CNN Classification For Automatic COVID-19 Diagnosis From CT-Scan Images On Small Dataset

arXiv 2108.07148 ↗First posted 16 Aug 2021eess.IV cs.CVcited by 4

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Abstract (arXiv)

We present an automatic COVID1-19 diagnosis framework from lung CT images. The focus is on signal processing and classification on small datasets with efforts putting into exploring data preparation and augmentation to improve the generalization capability of the 2D CNN classification models. We propose a unique and effective data augmentation method using multiple Hounsfield Unit (HU) normalization windows. In addition, the original slice image is cropped to exclude background, and a filter is applied to filter out closed-lung images. For the classification network, we choose to use 2D Densenet and Xception with the feature pyramid network (FPN). To further improve the classification accuracy, an ensemble of multiple CNN models and HU windows is used. On the training/validation dataset, we achieve a patient classification accuracy of 93.39%.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 16 Aug 2021.

Builds on

15 of 31 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Densely Connected Convolutional Networkscited by 6,691
  3. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  5. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  6. 2021MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysiscited by 72
  7. 2020COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Imagescited by 39
  8. 2020COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Imagescited by 34
  9. 2021COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learningcited by 29
  10. 2020Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classificationcited by 22
  11. 2020COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scanscited by 13
  12. 2021Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scanscited by 13
3 more
  1. 2021Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learningcited by 8
  2. 2020CT-CAPS: Feature Extraction-based Automated Framework for COVID-19 Disease Identification from Chest CT Scans using Capsule Networkscited by 7
  3. 2021A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Imagescited by 6

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Citing papers per year: 2023: 3; 2024: 0; 2025: 1.

4 papers.

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