# Strong Baseline and Bag of Tricks for COVID-19 Detection of CT Scans

arXiv: https://arxiv.org/abs/2303.08490 · HTML: https://mlsanity.com/paper/2303.08490/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [High-Performance Large-Scale Image Recognition Without Normalization](https://mlsanity.com/paper/2102.06171/) (2021)
- [Deep Transparent Prediction through Latent Representation Analysis](https://mlsanity.com/paper/2009.07044/) (2020)
- [MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis](https://mlsanity.com/paper/2106.07524/) (2021)

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

- [Advancing COVID-19 Detection in 3D CT Scans](https://mlsanity.com/paper/2403.11953/) (2024)
- [Advancing Lung Disease Diagnosis in 3D CT Scans](https://mlsanity.com/paper/2507.00993/) (2025)
- [Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling](https://mlsanity.com/paper/2507.01564/) (2025)
- [COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanism](https://mlsanity.com/paper/2403.11505/) (2024)

## Abstract (arXiv)

This paper investigates the application of deep learning models for lung Computed Tomography (CT) image analysis. Traditional deep learning frameworks encounter compatibility issues due to variations in slice numbers and resolutions in CT images, which stem from the use of different machines. Commonly, individual slices are predicted and subsequently merged to obtain the final result; however, this approach lacks slice-wise feature learning and consequently results in decreased performance. We propose a novel slice selection method for each CT dataset to address this limitation, effectively filtering out uncertain slices and enhancing the model's performance. Furthermore, we introduce a spatial-slice feature learning (SSFL) techniquecitehsu2022 that employs a conventional and efficient backbone model for slice feature training, followed by extracting one-dimensional data from the trained model for COVID and non-COVID classification using a dedicated classification model. Leveraging these experimental steps, we integrate one-dimensional features with multiple slices for channel merging and employ a 2D convolutional neural network (CNN) model for classification. In addition to the aforementioned methods, we explore various high-performance classification models, ultimately achieving promising results.