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<p>A research method has been developed using an information-extreme intelligent data analysis technology, which is based on maximizing the information capacity of the system during machine learning. The method was developed within the framework of a functional approach to modeling the cognitive processes of natural intelligence. Using information-extreme machine learning, it became possible to distinguish adenoma from early-stage cancer in prostate tissues based on whole-slide histological images. The sizes of the affected glands and their center-to-center distance were used as additional recognition meta-features, which made it possible to construct highly reliable decision rules in the machine-learning process.</p>

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Decision Support System for Diagnosing Early Stages of Prostate Cancer Based on Pathomorphological Features

  • 期刊论文

  • 发表日期：

    2026年1月1日

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  • A. S. Dovbysh,
  • A. M. Romaniuk,
  • I. V. Shelehov,
  • R. A. Moskalenko,
  • T. R. Savchenko,
  • A. P. Denysenko

摘要

A research method has been developed using an information-extreme intelligent data analysis technology, which is based on maximizing the information capacity of the system during machine learning. The method was developed within the framework of a functional approach to modeling the cognitive processes of natural intelligence. Using information-extreme machine learning, it became possible to distinguish adenoma from early-stage cancer in prostate tissues based on whole-slide histological images. The sizes of the affected glands and their center-to-center distance were used as additional recognition meta-features, which made it possible to construct highly reliable decision rules in the machine-learning process.