Automated Pneumonia Detection From Chest X-ray Images Using Machine Learning

Abstract

In this data science project, pneumonia detection was addressed using Convolutional Neural Networks (CNNs) applied to chest X-ray images. With the advancement of deep learning techniques, CNNs have emerged as a powerful tool for image classification tasks. By leveraging the capabilities of CNNs, this research aims to develop a robust and automated approach to classifying pneumonia from chest X-ray images, enabling timely and accurate diagnosis. The study includes comprehensive dataset details, explores supervised learning principles, and delves into binary classification techniques. Additionally, the research thoroughly examines the impact of different image dimensions on the model’s performance, while utilizing regularization to prevent overfitting. The developed CNN model achieves high accuracy on both the training and validation datasets, showcasing its potential in pneumonia detection. In addition to the technical aspects, potential applications in medical imaging are highlighted, limitations are addressed, and areas for improvement are proposed in this research. While the CNN model shows promise, it is designed as a valuable aid to medical professionals, enhancing early detection and screening processes.