International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 08 | Aug 2025
p-ISSN: 2395-0072
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A Survey on Ensemble Learning Techniques for Medical Image Classification Arumbaka Naveen1 , Dr. K. Jhansi Rani 2 1M. Tech, Computer and communication, ECE, JNTU Kakinada, Andhra Pradesh, India 2Assistant Professor, Dept of ECE, JNTU KAKINADA, Andhra Pradesh, India
------------------------------------------------------------------------***---------------------------------------------------------------------(DL), automated approaches have been increasingly Abstract: In recent years, ensemble learning has emerged
explored to support radiologists in improving diagnostic accuracy and consistency. Convolutional Neural Networks (CNNs) have demonstrated significant success in medical image analysis, particularly for classification tasks involving CXRs. Popular architectures such as AlexNet, VGGNet, ResNet, and Inception have been adapted and fine-tuned using large annotated datasets such as CheXpert, ChestX-ray14, and the COVID-19 Radiography Database.
as a powerful methodology in medical image analysis, particularly in the classification of chest X-ray images for diagnostic purposes. Ensemble methods such as bagging, boosting, and stacking aim to improve the reliability, accuracy, and generalization of classification models by combining the outputs of multiple learners. These techniques address common challenges in medical imaging, including data imbalance, overfitting, and variability in clinical image quality. This survey presents a comprehensive study of ensemble learning methods applied to chest X-ray classification tasks, highlighting architectural variations, model integration strategies, and evaluation metrics. Special focus is given to multi-class classification scenarios involving diseases such as COVID19, pneumonia, and tuberculosis. The paper reviews key developments in ensemble-based approaches using convolutional neural networks, discusses trade-offs in accuracy, complexity, and interpretability, and examines their applicability in clinical workflows. Finally, it outlines the practical challenges, potential solutions, and future research directions for deploying ensemble models in realworld medical diagnostic systems.
However, despite their effectiveness, individual CNNs often face limitations related to generalization, overfitting, and sensitivity to class imbalance, noise, or imaging artifacts. These issues can reduce their reliability in real-world clinical scenarios. In response to these challenges, ensemble learning has emerged as a promising strategy to enhance the robustness and accuracy of medical image classifiers. By aggregating the predictions of multiple models, ensemble methods help reduce variance and bias, improve generalization, and mitigate the risks associated with single-model predictions. This survey provides a detailed analysis of ensemble learning techniques as applied to chest X-ray image classification. It focuses on key ensemble methods— bagging, boosting, and stacking—and reviews their theoretical foundations, practical implementations, and comparative performance in medical imaging tasks. While each technique offers distinct advantages, their combined goal is to improve classification outcomes in critical healthcare settings.
Keywords: Boosting, Bagging, Stacking, Medical Image Analysis, Ensemble Learning.
1.Introduction: Chest radiography (commonly referred
to as chest X-rays or CXRs) continues to serve as one of the most accessible and widely used diagnostic imaging techniques in clinical practice. It plays a vital role in the detection and monitoring of various pulmonary diseases, including pneumonia, tuberculosis, lung cancer, and more recently, COVID-19. Despite its clinical importance, interpreting chest X-rays remains a challenging and subjective task, often influenced by inter-observer variability, subtle abnormalities, and the need for specialized expertise.
The paper further explores how ensemble strategies have been used to address specific diagnostic challenges in multi-class classification problems and reviews studies that have applied these techniques for disease detection, such as COVID-19, pneumonia, and tuberculosis. Methodological differences, dataset characteristics, and performance metrics are discussed to provide a comparative overview. In addition, the survey highlights
With the advancement of artificial intelligence (AI), particularly machine learning (ML) and deep learning
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