Deep Learning Application for Analyzing of Medical Images

Abstract

All of the research publications describe, emphasise, and classify one of the constituent aspects of deep learning models (DL) employed in medical image interpretation, but they do not provide a unified picture of the importance and impact of each constituent on DL model performance. Deep learning (DL) has experienced an exponential development of medicine, but applications in interpretations of medical imaging are in continuous development.  Our paper is unique in that it takes a unitary strategy to the constituent elements of DL models, such as data, tools used by DL architectures, or specifically constructed DL architecture combinations, and highlights their "key" features for completing tasks in current applications in medical image interpretation. Future study could focus on the utilisation of "key" properties particular to each ingredient of DL models, as well as the correct determination of their correlations, with the goal of improving the performance of DL models in the interpretation of medical pictures.