# A Light CNN for detecting COVID-19 from CT scans of the chest

arXiv: https://arxiv.org/abs/2004.12837 · HTML: https://mlsanity.com/paper/2004.12837/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 16 of 16)

- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning](https://mlsanity.com/paper/2104.11949/) (2021)
- [COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings](https://mlsanity.com/paper/2206.15073/) (2022)
- [Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans](https://mlsanity.com/paper/2101.07612/) (2021)
- [CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images](https://mlsanity.com/paper/2111.08710/) (2021)
- [Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring](https://mlsanity.com/paper/2105.15094/) (2021)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection](https://mlsanity.com/paper/2403.14002/) (2024)
- [COVID-19 Detection through Deep Feature Extraction](https://mlsanity.com/paper/2111.10762/) (2021)
- [COVID-19 Disease Identification on Chest-CT images using CNN and VGG16](https://mlsanity.com/paper/2207.04212/) (2022)
- [Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation](https://mlsanity.com/paper/2208.04718/) (2022)
- [Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms](https://mlsanity.com/paper/2303.17593/) (2023)
- [MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis](https://mlsanity.com/paper/2304.13135/) (2023)
- [Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images](https://mlsanity.com/paper/2311.08524/) (2023)
- [Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images](https://mlsanity.com/paper/2401.07020/) (2024)
- [A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images](https://mlsanity.com/paper/2501.17160/) (2025)

## Abstract (arXiv)

OVID-19 is a world-wide disease that has been declared as a pandemic by the World Health Organization. Computer Tomography (CT) imaging of the chest seems to be a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. Deep Learning has been extensively used in medical imaging and convolutional neural networks (CNNs) have been also used for classification of CT images. We propose a light CNN design based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with other CT images (community-acquired pneumonia and/or healthy images). On the tested datasets, the proposed modified SqueezeNet CNN achieved 83.00% of accuracy, 85.00% of sensitivity, 81.00% of specificity, 81.73% of precision and 0.8333 of F1Score in a very efficient way (7.81 seconds medium-end laptot without GPU acceleration). Besides performance, the average classification time is very competitive with respect to more complex CNN designs, thus allowing its usability also on medium power computers. In the next future we aim at improving the performances of the method along two directions: 1) by increasing the training dataset (as soon as other CT images will be available); 2) by introducing an efficient pre-processing strategy.