Project Abstract

Abstract
In recent years, the field of radiography has witnessed significant advancements in technology, particularly in the development of artificial intelligence (AI) algorithms for automated detection of abnormalities in radiographic images. This research project aims to contribute to this growing field by developing a novel AI algorithm specifically tailored for the automated detection of abnormalities in radiographic images. The proposed algorithm will utilize deep learning techniques to analyze and interpret radiographic images with a high level of accuracy and efficiency. The research will begin with a comprehensive review of existing literature on AI algorithms for medical image analysis, focusing on their applications in radiography and the challenges faced in automated detection of abnormalities. This literature review will provide a solid foundation for the development of the proposed AI algorithm. The methodology of the research will involve the collection of a large dataset of radiographic images containing both normal and abnormal cases. These images will be pre-processed and annotated to prepare them for training the AI algorithm. The algorithm will be trained using deep learning frameworks such as convolutional neural networks (CNNs) to learn and identify patterns indicative of abnormalities in radiographic images. The research findings will be presented and discussed in Chapter Four, where the performance of the developed AI algorithm will be evaluated based on metrics such as accuracy, sensitivity, specificity, and computational efficiency. The results will be compared with existing approaches to showcase the effectiveness and potential of the proposed algorithm. In conclusion, this research project aims to make a significant contribution to the field of radiography by developing an AI algorithm for automated detection of abnormalities in radiographic images. The proposed algorithm has the potential to enhance the accuracy and efficiency of radiographic image analysis, leading to improved diagnostic outcomes and patient care. The findings of this research will be valuable for healthcare professionals, researchers, and developers working in the field of medical imaging and artificial intelligence.

Project Overview