Semi-supervised multi-class pneumonia classification using a CNN-cascade forest framework

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.