Architecture of a Computer Decision Support System for CADx Breast Cancer

Prospective clinical studies have demonstrated increased detection rates of breast cancer using computer-aided diagnosis (CADx). CADx technology is designed to reduce observational errors made by physicians and reduce the number of false negatives when interpreting medical data. In this paper, we present the architecture of the computer-based breast cancer decision support system CADx, which allows for the construction of such a system. A feature of our proposed architecture is the creation of a corporate cloud for deploying a specialized neural network for CADx of primary breast cancer and hosting a non-relational factual database of patient research results, which can be used as SaaS for machine learning in computer diagnostic systems. Cloud technologies make it possible to use virtually unlimited memory resources and the speed of remote servers in SaaS mode, which requires the direct user only to have a fairly fast Internet connection and does not require a powerful computer or other equipment. The combination of machine learning methods with cloud service architecture creates a powerful new tool for effective diagnosis of breast cancer that can be used everywhere, and does not require significant costs for either deployment or operation. The database contains a heterogeneous vector of primary measurements (metadata, DICOM files, and other images and data) for each patient, which facilitates the construction of a neural network for tumor recognition and preliminary classification. We have populated the database with new region-specific data regarding the health of women in Ukraine under the severe stress caused by the ongoing war.