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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Data availability
The datasets used and analysed during the current study available from kaggle repository [https://www.kaggle.com/datasets/andrewmvd/covid19-ct-scans], https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia.
References
Zhang, Z., Liu, C., Hao, F. & Liu, Z. Style-transfer-based unsupervised change detection from heterogeneous images. IEEE Trans. Aerosp. Electron. Syst. 61 (3), 6537–6550. https://doi.org/10.1109/TAES.2025.3529431 (2025).
Gu, K. et al. Perceptual information fidelity for quality estimation of industrial images. IEEE Trans. Circuits Syst. Video Technol. 35 (1), 477–491. https://doi.org/10.1109/TCSVT.2024.3454160 (2025).
Alazeb, A. et al. Remote intelligent perception system for multi-object detection. Front. Neurorob. 18, Article 1398703. https://doi.org/10.3389/fnbot.2024.1398703 (2024).
Li, S. et al. Cost-sensitive neighborhood granularity selection for hierarchical classification. IEEE Trans. Knowl. Data Eng. 37 (8), 4471–4484. https://doi.org/10.1109/TKDE.2025.3566038 (2025).
Yin, L. et al. Convolution–transformer for image feature extraction. Comput. Model. Eng. Sci. 141 (1), 87–106. https://doi.org/10.32604/cmes.2024.051083 (2024).
Zhou, G. et al. A multi-scale enhanced feature fusion model for aircraft detection from SAR images. Int. J. Digit. Earth. 18 (1), Article 2507842. https://doi.org/10.1080/17538947.2025.2507842 (2025).
Xu, J. et al. How to characterize imprecision in multi-view clustering? In IEEE Transactions on Emerging Topics in Computational Intelligence. Advance online publication. https://doi.org/10.1109/TETCI.2025.3572135 (2025).
Zuo, Y. et al. Learning guided implicit depth function with scale-aware feature fusion. IEEE Trans. Image Process. 34, 3309–3322. https://doi.org/10.1109/TIP.2025.3570571 (2025).
Zhong, F. et al. Scattering characteristics guided network for ISAR space target component segmentation. IEEE Geosci. Remote Sens. Lett. 22, 1–5. https://doi.org/10.1109/LGRS.2025.3576662 (2025).
Tan, L. Causally informed instance-wise feature selection for explaining visual classifiers. Entropy. 27 (8), Article 814. https://doi.org/10.3390/e27080814 (2025).
Ho, T. T. et al. An unsupervised image registration method employing chest computed tomography images and deep neural networks. CBM. 154, 106612. https://doi.org/10.1016/j.compbiomed.2023.106612 (2023).
Yuyang et al. An unsupervised learning approach to diagnosing alzheimer’s disease using brain magnetic resonance imaging scans. IJMI. 173, 105027. https://doi.org/10.1016/j.ijmedinf.2023.105027 (2023).
Alexandre et al. Unsupervised machine learning driven analysis of verbatims of treatment-resistant schizophrenia patients having followed avatar therapy. JPM. 13(5), 801. https://doi.org/10.3390/jpm13050801 (2023).
Chen, D. et al. Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning. SPM. 40(1), 134–147. https://doi.org/10.1109/MSP.2022.3205430 (2024).
Delin, S. et al. Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach. Neuropsychopharmacology 48, 317–326. https://doi.org/10.1038/s41386-022-01457-4 (2023).
Xin et al. An unsupervised multi-focus image fusion method based on transformer and U-Net. IET IP. 17, 733–746. https://doi.org/10.1049/ipr2.12668 (2023).
Michelle Torres et al. A framework for the unsupervised and semi-supervised analysis of visual frames. PA. 32(2), 199–220. https://doi.org/10.1017/pan.2023.32 (2024).
Yizhang et al. An unsupervised Transformer-Based multivariate alteration detection approach for change detection in VHR remote sensing images. IEEE JSTAEO. 17, 3251–3261. https://doi.org/10.1109/JSTARS.2024.3349775 (2024).
Lagemann, C. et al. Challenges of deep unsupervised optical flow Estimation for particle-image velocimetry data. EF. 65, 30. https://doi.org/10.1007/s00348-024-03768-2 (2024).
Divya Mishra et al. Unsupervised image super-resolution for root hair enhancement and improved root traits measurements. IEEE TAE. 2 (1), 81–90. https://doi.org/10.1109/TAFE.2024.3359660 (2024).
Yaxin et al. Enhanced unsupervised image registration via dense U-Net and channel attention. JCSSA. 4 (5), 8–15. https://doi.org/10.5281/zenodo.13643091 (2024).
Waczak, J. et al. Unsupervised characterization of water composition with UAV-Based hyperspectral imaging and generative topographic mapping. RS. 16(13), 2430. https://doi.org/10.3390/rs16132430 (2024).
Cristiano, N. et al. From superpixels to foundational models: an overview of unsupervised and generalizable image segmentation. CG. 123, 104014. https://doi.org/10.1016/j.cag.2024.104014 (2024).
Christina, M. et al. Unsupervised machine learning methods and emerging applications in healthcare. KSSTA. 31, 376–381. https://doi.org/10.1007/s00167-022-07233-7 (2023).
Haohui et al. Unsupervised machine learning for disease prediction: a comparative performance analysis using multiple datasets. HT. 14, 141–154. https://doi.org/10.1007/s12553-023-00805-8 (2024).
https://www.kaggle.com/datasets/andrewmvd/covid19-ct-scans (accessed 6 May 2024).
https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia (accessed 8 May 2024).
Umirzakova, S., Shakhnoza, M., Sevara, M. & Whangbo, T. K. Deep learning for multiple sclerosis lesion classification and stratification using MRI. Comput. Biol. Med. 192, 110078 (2025).
Patrik Szepesi et al. Detection of pneumonia using convolutional neural networks and deep learning. BBE. 42(3), 1012–1022. https://doi.org/10.1016/j.bbe.2022.08.001 (2022).
Goyal, S. et al. Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques. JAIHC. 14, 3239–3259. https://doi.org/10.1007/s12652-021-03464-7
An, Q. et al. A deep convolutional neural network for pneumonia detection in X-ray images with attention ensemble. Diagnostics. 14 (4), 390. https://doi.org/10.3390/diagnostics14040390 (2024).
Zhang, D. et al. Pneumonia detection from chest X-ray images based on convolutional neural network. Electronics. 10(13), 1512. https://doi.org/10.3390/electronics10131512 (2021).
Shah, P. M. et al. Deep GRU-CNN model for COVID-19 detection from chest X-Rays data. Access. 10, 35094–35105. https://doi.org/10.1109/ACCESS.2021.3077592 (2022).
Kavitha, M. et al. Automated lung disease detection, classification and prediction using RNN framework. ICECS’24. 491, 03015. https://doi.org/10.1051/e3sconf/202449103015 (2024).
Singh, S. et al. Efficient pneumonia detection using vision Transformers on chest X-rays. SR 14 (1), 2487. https://doi.org/10.1038/s41598-024-52703-2 (2024).
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P.M. wrote the main manuscript text and T.Y., R.K. prepared Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11. All authors reviewed the manuscript.
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Muthukumaraswamy, P., Yuvaraj, T. & Krishnamoorthy, R. Semi-supervised multi-class pneumonia classification using a CNN-cascade forest framework. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38849-1
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DOI: https://doi.org/10.1038/s41598-026-38849-1
