CANCER DIAGNOSIS WITH IMAGE FILTER-INTEGRATED ARTIFICIAL INTELLIGENCE ALGORITHMS: INNOVATIVE POSSIBILITIES FOR MELANOMA DETECTION WITH A HIGH DEGREE OF ACCURACY

This paper makes use of image filter-generated high dimensional data with artificial intelligence algorithms for an innovative and accurate diagnosis of cancer. The paper describes two methods, the first of which employs image filters to extract, from images, hundreds (and even thousands) of quantified features representing high dimensional data that can be fed into selection and classification algorithms to accurately diagnose cancer. The method is applicable to many different cancer-related cases. We have used this method of image filter integrated artificial intelligence algorithms in the context of a data set to achieve up to 100 % accuracy. This method, which is static and hence focuses on cases at a point in time, could be generalized to a dynamic setting by collecting data at different points in time and evaluating them algorithmically so as to construct a trajectory describing cancer progression over time. Obtaining such a cancer trajectory would facilitate the decisions for optimal treatment and/or interventions to slow down cancer progression or help eradicate cancer altogether. Overall, methods are flexible enough to handle a broad range of cases with various levels of complexity and intricacy as well as varying scale and scope associated with different types, subtypes, degrees and stages of cancer.