Pulmo Scan: A Deep Learning Framework for Pneumonia Detection using X-Ray Images

Abstract

Pneumonia is an acute respiratory infection of the lung that must be identified at its early stages to keep mortality rates to a minimum, especially in Wireless Body Area Networks (WBAN). Traditional diagnosing methods, i.e., manual interpretation of X-rays, are time-consuming and prone to human errors. The existing models are plagued by generalizability issues, dataset imbalance, and a high false-detection rate, which complicate pneumonia classification. To address these challenges, we propose a CNN-based model that leverages transfer learning to improve detection accuracy. The model consists of three convolutional layers, dropout regularisation, the Adam optimiser, and robust data augmentation methods to learn improved features and prevent overfitting. We trained the model on the Chest X-ray dataset (NORMAL vs. PNEUMONIA) containing 5,863 images. We achieved enhanced accuracy across five state-of-the-art models in our experiments, with higher precision, recall, and F1 Scores. Additionally, the model generalises well by leveraging diverse preprocessing techniques, including image resizing, normalisation, and various forms of augmentation. Compared with existing architectures such as VGG-16, ResNet50, and InceptionV3, the model demonstrated improved robustness and classification accuracy. This research facilitates the development of a solid deep learning framework for detecting pneumonia to be incorporated into real-time medical software.