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

arXiv: https://arxiv.org/abs/2108.07148 · HTML: https://mlsanity.com/paper/2108.07148/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)
- [COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images](https://mlsanity.com/paper/2009.05383/) (2020)
- [COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images](https://mlsanity.com/paper/2004.02696/) (2020)
- [COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning](https://mlsanity.com/paper/2101.07433/) (2021)
- [Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification](https://mlsanity.com/paper/2009.07652/) (2020)
- [COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans](https://mlsanity.com/paper/2010.16041/) (2020)
- [Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans](https://mlsanity.com/paper/2101.05442/) (2021)
- [Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learning](https://mlsanity.com/paper/2104.05121/) (2021)
- [CT-CAPS: Feature Extraction-based Automated Framework for COVID-19 Disease Identification from Chest CT Scans using Capsule Networks](https://mlsanity.com/paper/2010.16043/) (2020)
- [A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images](https://mlsanity.com/paper/2106.14403/) (2021)

## Built on by (most cited first, 4 of 4)

- [Qualitative Data Augmentation for Performance Prediction in VLSI circuits](https://mlsanity.com/paper/2302.07566/) (2023)
- [AutoLungDx: A Hybrid Deep Learning Approach for Early Lung Cancer Diagnosis Using 3D Res-U-Net, YOLOv5, and Vision Transformers](https://mlsanity.com/paper/2305.00046/) (2023)
- [Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation](https://mlsanity.com/paper/2310.10691/) (2023)
- [Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation](https://mlsanity.com/paper/2510.08116/) (2025)

## 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%.