Hybrid Ensemble Learning for Multi-Class Chest X-Ray Classification Using Deep CNN
Abdul Rehman Khan TareenMuhammad Laiq Ur Rahman ShahidMuhammad Hamza ZafarSoban Abu Khifs
F. Shaukat
Jul 2026· Allied Medical Research Journal· 0 citations· 18 references
TL;DR
A hybrid ensemble learning approach to classify chest X-ray images into four classes—Normal, COVID-19, Pneumonia, and Tuberculosis exhibited high sensitivity in detecting Tuberculosis with considerable stability in classifying Normal, COVID-19, and Pneumonia.
Abstract
Background: COVID-19, pneumonia, and TB (tuberculosis) are still the big killers of people suffering from chest disease and continue to be a serious health challenge globally. A timely diagnosis leads to timely treatment and improved patient outcomes. Chest X-ray (CXR) imaging is widely used for diagnostic purposes due to its speed, low cost, and availability in most healthcare facilities. Manual reading of CXR images is, however, challenging because of the similarity in the presentation of radiographic features across various chest diseases.
Methods: This research introduces a hybrid ensemble learning approach to classify chest X-ray images into four classes—Normal, COVID-19, Pneumonia, and Tuberculosis. Three Deep CNN network models, namely Xception, AlexNet, and EfficientNet-B0, were used for deep feature extraction. Additionally, texture features of the images were extracted using Gabor filters. The deep and texture features were combined and classified using logistic regression and a stacking ensemble learning approach. A publicly available chest X-ray image database containing 7,135 X-rays was used, with six-fold stratified cross-validation to assess the proposed approach.
Results: The ensemble models outperformed the individual CNN models. The Average Ensemble produced the best results with an accuracy of 91.18%, an Average Precision (AP) of 96.75%, and an Area Under the ROC Curve (AUC) of 98.87%. The proposed model performs well across all four disease classes. It exhibited high sensitivity in detecting Tuberculosis with considerable stability in classifying Normal, COVID-19, and Pneumonia.
Conclusion: The proposed framework demonstrates the effectiveness of integrating deep learning features, Gabor texture features, and ensemble learning for improved chest X-ray image classification. This can help computer-assisted diagnostics systems and aid medical workers in identifying chest diseases early.
A Hybrid Prototype Fusion Network (HPFN) developed based on the ConvNeXt-Tiny architecture is proposed, and deep feature representation and prototype-based similarity calculation are combined in a single decision mechanism, outperforming both the compared deep learning architectures and traditional feature-based methods.
Pneumonia remains one of the leading causes of respiratory illness worldwide and requires timely diagnosis to reduce
disease severity and mortality. Interpretation of chest X-ray images is a routine diagnostic procedure; however, manual
examination can be time-consuming and is influenced by the experience of radiologists. Computer-aided diagnostic systems
based on artificial intelligence have therefore attracted considerable attention as supportive tools for clinical decision-making.
This research presents a hybrid framework that combines deep feature extraction with traditional machine learning techniques
for automated pneumonia detection from chest X-ray images. Initially, chest X-ray images are preprocessed and analysed using
an EfficientNetV2 convolutional neural network to learn representative image features. Instead of directly performing end-toend classification, the extracted deep features are used to train three supervised machine learning classifiers: Random Forest,
Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). This hybrid strategy combines the feature
representation capability of deep learning with the classification efficiency of conventional machine learning algorithms. To
improve model transparency, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to highlight image regions
that contribute most to the prediction. Furthermore, the selected classifier is integrated into a Streamlit-based web application
that enables users to upload chest X-ray images and obtain real-time diagnostic predictions.
Experimental evaluation demonstrates that the Support Vector Machine classifier achieved the highest performance among the
evaluated models, with an accuracy of 89.74%, precision of 86.22%, recall of 99.49%, F1-score of 92.38%, and ROC-AUC of
98.21%, indicating strong capability for distinguishing pneumonia from normal chest radiographs. The proposed framework
illustrates that combining deep feature extraction with machine learning classifiers can provide accurate, interpretable, and
computationally efficient pneumonia detection while supporting practical clinical deployment.
K. Veen, Pravitha R Prasad· International Journal for Re...· 0 citations
Pulmonary infections, especially pneumonia constitute a major worldwide health burden and need early and accurate diagnosis in order to minimize disease severity and death. Chest radiographs are regularly performed for screening of pulmonary infections; however, the interpretation of the radiographs by manual techniques is time consuming and subject to inter-observer variability, and this has motivated the need for automated and reliable diagnostic systems. In the current work, a hybrid deep feature learning framework for clinical-level differentiation of pulmonary infections from the chest X-ray images is proposed by effectively incorporating local and global feature representations. The proposed model consists of EfficientNet-B3 for extracting deep spatial features and a Vision Transformer to extract long-range contextual dependencies, a feature fusion strategy towards better representation learning. The framework is tested on a publicly available chest X-ray dataset of Normal and Pneumonia classes. Experimental results show the high classification accuracy of the proposed hybrid model is 98.3% with high precision, recall, F1-score, and AUC performance compared with traditional CNN, ResNet, auto-encoder and transformer-based model. The results underscore the clinical reliability, good generalization capacity and possible applicability of the proposed framework to automated pulmonary infection screening and decision support systems.
T. Srinivas, Faaleha Heba Fakruddin, Mettu Jhansi Rani et al.· 2026 International Conferenc...· 0 citations
The rising incidence of pneumonia and tuberculosis presents major public health challenges, particularly in resource-limited regions such as Ethiopia, where shortages of skilled radiologists hinder timely diagnosis. Although deep learning has shown promise in medical image analysis, robust multiclass classification of these diseases from chest X-ray images remains technically challenging. This study proposes a deep learning–based multiclass classification framework for automated detection of pneumonia and tuberculosis from chest X-rays. A total of 2,100 chest X-ray images were collected from major hospitals in the Amhara region. Images were preprocessed and augmented to improve robustness. Multiple convolutional neural network architectures including Sequential CNN, VGG19, MobileNetV3-Large, EfficientNetV2-B0/B1, and InceptionResNet were trained and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. A two-stage transfer learning strategy with systematic hyperparameter optimization was applied. Among the evaluated models, the customized VGG19 achieved the best performance, with 99% accuracy, precision, recall, and F1-score, and an AUC of 99.93%. Other models also demonstrated strong performance, with accuracies ranging from 95 to 98%. These results highlight the importance of model selection and optimization in medical image classification. An independent external validation using 3,568 chest X-ray images demonstrated robust generalization, with an accuracy of 88.83% and macro F1-score of 83.92%. Future work will focus on prospective multicenter validation, patient-level evaluation, and expert radiologist verification of the explainability framework.
Addisu Baye Flatie, Zegeye Regasa Wordofa, Abraham Keffale Mengistu et al.· Discover Artificial Intellig...· 0 citations
: Pneumonia continues to be a major source of morbidity and mortality globally, especially in developing countries where a shortage of specialists makes radiological assessment challenging. Patients' survival and appropriate treatment depend on a timely and accurate diagnosis. This study examines a deep learning technique called a Convolutional Neural Network (CNN) that looks at chest X-rays to identify pneumonia. To accurately classify normal and pneumonia-infected lungs, the proposed system makes use of sophisticated picture preprocessing, data augmentation, and improved CNNs to find discriminative spatial characteristics. Using pre-trained models like VGG16, ResNet50, and DenseNet121, it also investigated transfer learning to improve model performance and generalization with little datasets. The CNN model demonstrated 96.8 accuracy, 95.6 precision, 97.3 recall, and 96.4 F1-score on the publicly available Chest X-Ray (Pneumonia) dataset. The results suggest that CNN-based models have the capacity to aid radiologists in the early diagnosis to ease the medical intervention and minimize diagnostic errors. This study is part of the developing body of AI-based healthcare diagnostics, and it highlights the possibility of deep learning in medical image processing and disease prediction.
M. Devi, Tanya, Aradhya Mittal et al.· Proceedings of the 1st Inter...· 0 citations
The proposed deep learning models provide an efficient and accurate tool for multiclass pneumonia detection from CXR images and have the potential to support healthcare professionals in making more accurate diagnoses.
Timothy Karani, Stephen Waithaka· Journal of the Kenya Nationa...· 0 citations
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