Authors : Dr. S. Rajalakshmi; Srinivasan K.; Lavanya J.; Pooja T. S. R.; Raajalakshimi R.; Sabeeha Farheen; Sakthisri A.; Shrmila A.

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/39b4kkjs

Scribd : https://tinyurl.com/42ne6drs

DOI : https://doi.org/10.38124/ijisrt/26feb917

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Ensemble Approach for Enhanced COVID-19 Detection using Chest X-Rays introduces an automated classification system that detects COVID-19 in chest X-rays by employing hierarchical classification and augmented images. Using a majority vote-based ensemble of five distinct supervised algorithms, this system aggregates predictions to improve decision accuracy. By recognizing unique radiographic texture patterns associated with COVID-19 through advanced feature extraction techniques like statistical texturedescriptors, the model aids in early detection, addressing theurgent need for effective screening. The ensemble method enhances diagnostic accuracy and reliability by combining the strengths of multiple classifiers, each adding a unique perspective that strengthens the final prediction. Data augmentation adds robustness, compensating for variations in X-ray images and improving adaptability across diverse datasets. Additionally, identifying distinct texture patterns and refining feature extraction techniques contribute to a precise and consistent diagnostic model. This project underscores the transformative potential of machine learning-driven medical imaging, highlighting benefits such as speed, precision, and reliability. Ov erall, the proposed model not only meets the demand for efficient COVID-19 screening but also marks a significant advancement in automated infectious disease detection through medical imaging analysis.

Keywords : COVID-19 Detection, Chest X-Ray, Ensemble Approach, Supervised Algorithms, Feature Extraction, Data Augmentation, Medical Imaging, Diagnostic Accuracy.

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