Efficient Pneumonia Detection Using Vision Transformers on Chest X-Rays

Pneumonia remains a significant health concern worldwide, with timely and accurate diagnosis being crucial for effective treatment and patient recovery. Traditional methods of pneumonia diagnosis using chest X-rays often rely on manual interpretation by radiologists, which is time- consuming and prone to human error. In this project, we propose an Efficient Pneumonia Detection System leveraging Vision Transformers (ViTs) to analyze chest X-ray images and enhance diagnostic accuracy. ViTs, unlike convolutional neural networks (CNNs), utilize a self-attention mechanism that captures both global and local dependencies, enabling superior feature extraction and classification. The proposed model is trained on a large dataset of labeled chest X-ray images, ensuring robustness and generalizability. Extensive experimentation is conducted to optimize hyperparameters and improve model efficiency, resulting in a highly accurate system capable of distinguishing between normal and pneumonia- affected cases. The results demonstrate that the Vision Transformer-based approach outperforms traditional CNN models in terms of precision, recall, and overall classification accuracy. This system has the potential to assist healthcare professionals by providing an automated, reliable, and scalable solution for early pneumonia detection, ultimately reducing diagnostic delays and improving patient care.