Machine Learning Based X-RAY Prediction Model

Abstract

This study aimed to develop and evaluate a convolutional neural network (CNN) model for multi-disease classification using a large dataset of 53,000+ chest X-ray images. The CNN architecture was trained to predict the presence of 14 different diseases based on input chest X-ray images. Key findings indicate the model achieves competitive performance with high accuracy, demonstrating potential for automated disease diagnosis. Leveraging the power of deep learning, particularly CNNs, this study shows promising results in improving diagnostic processes in healthcare. Automating disease diagnosis using deep learning methods can significantly enhance the efficiency of healthcare systems, potentially reducing the burden on medical professionals and improving patient outcomes. The success of this CNN model in multi-disease classification based on chest X-ray images highlights the potential of artificial intelligence in revolutionizing diagnostic processes in healthcare, underscoring the importance and effectiveness of deep learning methods, particularly CNNs, in advancing medical diagnostics and improving patient care.