Detecting pneumonia, especially coronavirus disease 2019 (COVID-19), from chest X-ray (CXR) images is one of the most effective ways for disease diagnosis and patient triage. The application of deep neural networks (DNNs) for CXR image classification is limited due to the small sample size of the well-curated data. To tackle this problem, this article proposes a distance transformation-based deep forest framework with hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification. In our proposed method, hybrid features of CXR images are extracted in two ways: hand-crafted feature extraction and multigrained scanning. Different types of features are fed into different classifiers in the same layer of the deep forest (DF), and the prediction vector obtained at each layer is transformed to form distance vector based on a self-adaptive scheme. The distance vectors obtained by different classifiers are fused and concatenated with the original features, then input into the corresponding classifier at the next layer. The cascade grows until DTDF-HFF can no longer gain benefits from the new layer. We compare the proposed method with other methods on the public CXR datasets, and the experimental results show that the proposed method can achieve state-of-the art (SOTA) performance. The code will be made publicly available at https://github.com/hongqq/DTDF-HFF.
Citation
Hong, Q., Lin, L., Li, Z., Li, Q., Yao, J., Wu, Q., Liu, K., & Tian, J. (in press). A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification. IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/TNNLS.2023.3280646
Journal Article Type
Article
Acceptance Date
May 17, 2023
Online Publication Date
Jun 7, 2023
Deposit Date
Jan 19, 2024
Publicly Available Date
Mar 7, 2024
Journal
IEEE Transactions on Neural Networks and Learning Systems
