Decision support system for medical pathology recognition

Given article considers the problem of automation chromosome pathology recognition. Overview existing publications in adjacent topics has shown need for further research due to scientific relevance and lack groundwork. The application domain been considered - both theoretical aspects recognition diagnostics process itself. Classification main abnormalities performed. A formal algorithm derived from aforementioned analysis. version provided given publication is a high-level draft will be extended. Along with algorithm, requirements prototype software itself have designed. It should accept karyogram image as an input return information about anomaly output. Software implemented on Python programing language using OpenCV processing library. Prototype program library, allowing use it different contexts. For example, possible via terminal or design Web API wrapper around it. decision support system currently capable detecting quantity abnormalities. Results obtained paper prove possibility designing full-powered allow focus extending improving functionality each distinct step basic cover more abnormality types