# Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT

arXiv: https://arxiv.org/abs/2005.03264 · HTML: https://mlsanity.com/paper/2005.03264/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 13 of 13)

- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection](https://mlsanity.com/paper/2102.11099/) (2021)
- [COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers](https://mlsanity.com/paper/2207.09529/) (2022)
- [Deep Forest with Hashing Screening and Window Screening](https://mlsanity.com/paper/2207.11951/) (2022)
- [Machine learning applications for COVID-19: A state-of-the-art review](https://mlsanity.com/paper/2101.07824/) (2021)
- [Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification](https://mlsanity.com/paper/2210.05073/) (2022)
- [Improving Mutual Information based Feature Selection by Boosting Unique Relevance](https://mlsanity.com/paper/2212.06143/) (2022)
- [An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease](https://mlsanity.com/paper/2104.14116/) (2021)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP](https://mlsanity.com/paper/2103.09904/) (2021)
- [Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis](https://mlsanity.com/paper/2106.12313/) (2021)
- [A GA-like Dynamic Probability Method With Mutual Information for Feature Selection](https://mlsanity.com/paper/2210.11954/) (2022)
- [Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images](https://mlsanity.com/paper/2311.08524/) (2023)

## Abstract (arXiv)

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.