# COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images

arXiv: https://arxiv.org/abs/2312.07580 · HTML: https://mlsanity.com/paper/2312.07580/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [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)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging](https://mlsanity.com/paper/2206.04732/) (2022)
- [Visual Transformer with Statistical Test for COVID-19 Classification](https://mlsanity.com/paper/2107.05334/) (2021)
- [Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans](https://mlsanity.com/paper/2207.01579/) (2022)
- [FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis](https://mlsanity.com/paper/2207.01758/) (2022)
- [Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets](https://mlsanity.com/paper/2207.12218/) (2022)
- [Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images](https://mlsanity.com/paper/2111.11191/) (2021)
- [Two-Stage COVID19 Classification Using BERT Features](https://mlsanity.com/paper/2206.14861/) (2022)
- [COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Images](https://mlsanity.com/paper/2207.00259/) (2022)

## Built on by (most cited first, 2 of 2)

- [SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection](https://mlsanity.com/paper/2407.15728/) (2024)
- [Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A feasibility study](https://mlsanity.com/paper/2405.20562/) (2024)

## Abstract (arXiv)

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 by 224) were input into an Xception transfer learning model. Leveraging Xception's architecture and pre-trained weights, the modified model achieved binary classification. Promising results on the COV19-CT database showcased higher validation accuracy and macro F1 score at both the slice and patient levels compared to our previous solution and alternatives on the same dataset.