DESIGN AND IMPLEMENTATION OF MACHINE LEARNING BASED X-RAY SCANNING SYSTEM

Abstract The project titled Design and Implementation of Machine Learning Based X-Ray Scanning System aims to enhance the accuracy and efficiency of medical diagnostics by leveraging machine learning techniques for the analysis of X-ray images. The primary objective of this project is to develop a system that can automatically detect fractures in X-ray scans, thereby assisting radiologists in making timely and accurate diagnoses. In the background, the demand for efficient and reliable diagnostic tools has grown significantly due to the increasing volume of medical imaging data and the need for early detection of diseases. Traditional methods of X-ray analysis, which rely heavily on human expertise, are often time-consuming and prone to human error. This has necessitated the integration of advanced technologies, such as machine learning, to improve the precision and speed of diagnostic processes. The methodology involves the collection and preprocessing of X-ray images, followed by the development of a machine learning model capable of identifying abnormalities. The system employs convolutional neural networks (CNNs) for image classification and anomaly detection. The model is trained using a dataset of labeled X-ray images, and its performance is evaluated based on metrics such as accuracy, sensitivity, and specificity. The results of the project demonstrate the potential of the developed system to accurately detect abnormalities in X-ray scans, with an accuracy of 82% and a processing time of 10 mins. These findings highlight the system's effectiveness in reducing diagnostic errors and improving patient outcomes. In conclusion, the implementation of a machine learning-based X-ray scanning system has significant implications for the healthcare industry. The system not only enhances the diagnostic capabilities of radiologists but also provides a scalable solution for healthcare facilities to improve patient care. Future work could focus on expanding the system to handle a wider range of medical imaging modalities and integrating it with existing healthcare systems.