What question did this study set out to answer?

The aim is to improve pneumonia classification accuracy using a semi-supervised approach that handles sparse annotated data.

February 8, 2026Open Access

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

Key Points

The aim is to improve pneumonia classification accuracy using a semi-supervised approach that handles sparse annotated data.
Developed a CNN-Enhanced Cascade Forest (CE-Cascade) framework for pneumonia classification.
Extracted features from chest X-ray and CT images using a convolutional neural network.
Incorporated a semi-supervised pseudo-labelling strategy to utilize unlabelled data effectively.
Evaluated the model on a dataset of 4578 medical images categorized into pneumonia types and normal.
Achieved an overall classification rate of 98.86%, surpassing some leading deep learning models.
Demonstrated the effectiveness of combining CNN with Cascade Forest for robust pneumonia classification.

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.

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