Intelligent Pneumonia Detection System: Leveraging Machine Learning and Medical Imaging for Early Diagnosis

Effective treatment for pneumonia, a potentially fatal lung infection, depends on an early and precise diagnosis. Conventional diagnostic techniques, such manually interpreting chest X-rays, are frequently laborious and subjective. In this study, we use the XGBoost (Extreme Gradient Boosting) algorithm to give an automated method for autonomously using chest X-ray pictures for detecting pneumonia. XGBoost is a potent approach to learning that is extremely efficient in managing complicated datasets and is renowned for its high accuracy and efficiency. Our approach combines XGBoost for robust classification with deep learning for X-ray image extraction of attributes. The system is taught on a big collection of grouping chest X-ray pictures to ensure consistency in real-world applications. and its performance is assessed using precision, recall, F1-score, and AUC-ROC metrics. According to the experimental results, the pneumonia detection model based on XGBoost performs better than more Common statistical classifiers are Random Forest and Support Vector Machines (SVM). The model is an invaluable tool for helping radiologists with medical image analysis as it successfully lowers false positives and raises overall diagnosis accuracy. Furthermore, the high sensitivity and specificity of pneumonia case detection are guaranteed by XG Boost’s capacity to manage unbalanced data. This study exhibits the potential for merging gradient boosting methods with medical imaging, opening the door for AI-powered solutions that improve patient outcomes and early pneumonia detection.