XAI hybrid multi-staged algorithm for routine & quantum boosted oncological medical imaging
Ayesha Sohail, Mohamed Abdelsabour Fahmy, Usama Ahmad Khan
Abstract
Medical imaging is the process of visualizing the diseased part, inside the patient’s body, with the aid of images. The field of medical imaging depends on several disciplines of science and technology, including physics, biological sciences, engineering, artificial intelligence and mathematics. These disciplines contribute in designing the imaging devices, installation of the devices and the collection and analysis of the images for better understanding and future forecasting of the disease prognosis and prevention. In this manuscript, medical images are analyzed with the aid of the a new hybrid machine learning approach, where the breast cancer images are studied in a novel manner with the help of a newly devised algorithm that is conceptually more sound as compared to already existing algorithms. Step by step stages are followed by the algorithm to process, filter, segment, statistically analyze and to classify the medical images. The results from different classification tools are compared in a novel manner, inspired from the explainable artificial intelligence tools for classification. The algorithm devised during this research can serve as a useful tool, in the evolving field of particle - physics -imaging.