Pneumonia Detection from Chest XRay Images Using Deep Learning with Smart Validation

Pneumonia is a leading cause of death worldwide, and early detection is critical for effective treatment. This study presents a deep learning-based approach for detecting pneumonia from chest X-ray images, utilizing Convolutional Neural Networks (CNN), ResNet, and MobileNet models. The primary goal is to classify chest X-ray images as either 'Pneumonia' or 'Normal,' ensuring accurate diagnosis. To enhance the reliability of the system, a smart validation mechanism is integrated to detect irrelevant images, such as non-X-ray images. For this task, the MobileNet model is employed to distinguish irrelevant images from medical X-rays. This multi-step approach not only ensures accurate pneumonia detection but also prevents misclassification by filtering out irrelevant images. The models are trained and evaluated on a large dataset of chest X-ray images, achieving high accuracy and robustness in classification. The proposed system demonstrates potential for assisting healthcare professionals in the rapid and reliable detection of pneumonia, while also addressing the challenge of ensuring only valid medical images are processed.

Keywords: Pneumonia detection, Chest X-ray, Deep learning, CNN, ResNet, MobileNet, Image classification, Smart validation, Irrelevant image detection, Medical imaging.