Pneumonia Detection Using CNN Through Chest X-rays

Abstract

Pneumonia remains a significant cause of morbidity and mortality, with early and accurate detection being crucial for timely treatment and improved patient outcomes. X-ray imaging is a widely used diagnostic tool due to its accessibility and cost-effectiveness. In recent years, deep learning techniques have shown promising results in automating the detection of pneumonia from chest X-ray images. Our proposed methodology begins with data preprocessing and augmentation to enhance the model’s robustness and generalisation. The model is trained on a large annotated dataset of chest X-ray images, and its performance is evaluated using standard metrics such as accuracy and precision. The architecture of the model is designed to capture intricate patterns and features indicative of pneumonia, achieving high accuracy and robustness. Extensive experimentation and validation demonstrate that our CNN model achieves superior diagnostic performance compared to traditional methods. It exhibits high sensitivity and specificity, indicating its effectiveness in accurately identifying pneumonia cases. These results highlight the potential of CNN-based systems to assist radiologists by providing rapid and reliable diagnostic support, ultimately contributing to timelier and more effective patient care. This research underscores the transformative impact of artificial intelligence in medical imaging, paving the way for enhanced diagnostic capabilities and improved healthcare delivery.