Decision-making support system for diagnosis of oncopathologies by histological images

Anatoliy Dovbysh; Ihor Shelehov; Anatolii Romaniuk; Roman Moskalenko; Taras Savchenko

doi:10.1016/j.jpi.2023.100193

Decision-making support system for diagnosis of oncopathologies by histological images

J Pathol Inform. 2023 Jan 26:14:100193. doi: 10.1016/j.jpi.2023.100193. eCollection 2023.

Authors

Anatoliy Dovbysh¹, Ihor Shelehov¹, Anatolii Romaniuk², Roman Moskalenko², Taras Savchenko¹

Affiliations

¹ Department of Computer Science, Sumy State University, 2 Rymskogo-Korsakova Street, Sumy, Sumy Region, Ukraine.
² Department of Pathology, Sumy State University, 31 Pryvokzalna Street, Sumy, Sumy Region, Ukraine.

Abstract

The aim of the study is to increase the functional efficiency of machine learning decision support system (DSS) for the diagnosis of oncopathology on the basis of tissue morphology. The method of hierarchical information-extreme machine learning of diagnostic DSS is offered. The method is developed within the framework of the functional approach to modeling of natural intelligence cognitive processes at formation and acceptance of classification decisions. This approach, in contrast to neuronal structures, allows diagnostic DSS to adapt to arbitrary conditions of histological imaging and flexibility in retraining the system by expanding the recognition classes alphabet that characterize different structures of tissue morphology. In addition, the decisive rules built within the geometric approach are practically invariant to the multidimensionality of the diagnostic features space. The developed method allows to create information, algorithmic, and software of the automated workplace of the histologist for diagnosing oncopathologies of different genesis. The machine learning method is implemented on the example of diagnosing breast cancer.

Keywords: Breast cancer; Computer-aided detection; Hierarchical information‐extreme machine learning; Histological image; Information criterion; Machine learning.