A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification

Detecting pneumonia, especially 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 network for CXR image classification is limited due to the small sample size of the well-curated data.To tackle this problem, this paper proposes a distance transformation-based deep forest framework with the hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification.In our proposed method, hybrid features of CXR images are extracted by two ways: handcrafted feature extraction and multi-grained scanning.Different types of features are fed into different classifiers in the same layer of the deep forest, 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 the benefits from the new layer.We compare the proposed method with other methods on the public CXR data sets, and the experimental results show that the proposed method can achieve state-of-the art performance.