PneuX-Net An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images

The growing prevalence of pneumonia worldwide makes it a significant public health concern. Early detection of pneumonia can drastically improve treatment outcomes, especially in regions where access to medical professionals and diagnostic facilities is limited. However, manual diagnosis using X-ray images is time-consuming, prone to human error, and requires specialized expertise. This highlights the need for automated diagnostic systems that can quickly and accurately detect pneumonia from chest X-rays, reducing the burden on healthcare providers. By leveraging deep learning models like MobileNet, ResNet, and Xception, along with machine learning classifiers, this project aims to build a fast, accurate, and scalable pneumonia detection system that can assist medical professionals in diagnosing the disease more efficiently, ensuring timely treatment and better patient outcomes.