Semi-supervised multi-class pneumonia classification using a CNN-cascade forest framework.
Authors
Affiliations (3)
Affiliations (3)
- Department of Biomedical Engineering, Kings Engineering College, Chennai, 602117, India. [email protected].
- Department of Electrical and Electronics Engineering, Chennai Institute of Technology, Chennai, 600069, India.
- Department of Information Technology, Chennai Institute of Technology, Chennai, 600069, India.
Abstract
Medical imaging diagnosis of pneumonia must be accurate at distinguishing between the various disease subtypes but must be resistant to sparse annotated evidence as well as cross-imaging variability. The current deep learning methods are mostly univariate and binary-classification, which limits their clinical use. As a solution to these shortcomings, this paper suggests a semi-supervised CNN-Enhanced Cascade Forest (CE-Cascade) scheme to classify multi-class pneumonia based on X-ray and CT images of the chest. Based on the approach proposed, a convolutional neural network will first be used to test the medical images to extract discriminative deep features and the resultant features will be refined by a cascade forest to detect hierarchal and multi-scale patterns with great significance in terms of pneumonia detection. A semi-supervised pseudo-labelling strategy is also incorporated in order to competently utilize unlabelled data and better generalization with scarce annotation requirements. The framework is tested over a data set that consists of 4578 chest X-ray and CT images that are divided into categories of bacterial, viral, fungal, general pneumonia, and normal. The experimental findings prove that the given CE-Cascade model reaches the overall classification rate of 98.86 that is higher than some state-of-the-art deep learning systems. Findings validate that semi-supervised learning with the integration of CNN and Cascade Forest to create a robust and clinically meaningful method to classify multi-classes of pneumonia is automated.