Adaptive Feature Selection Guided Deep Forest for COVID 19 Classification With Chest CT
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) AUC, precision and F1 score achieved by our method are 91.79% 93.05% 89.95% 96.35% 93.10% and 93.07% 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.
IEEE Journal of Biomedical and Health Informatics