Classification and recognition of medical images based on the SGTM neuroparadigm
- Author(s)
- Viktor Khavalko, Ivan Tsmots, Anastasija Kostyniuk, Christine Strauss
- Abstract
The paper discusses methods and algorithms for medical images preprocessing, their classification and recognition, which are oriented to use in machine vision systems. The structure and description of a number of software subsystems of image processing have been developed. The paper considers and analyzes the effectiveness of using methods for improving the visual quality of images as a stage of images pre-processing before classification. It is shown that image pre-processing is an effective and has significant impact on the accuracy of the images classification. The simulation of methods for improving the images’ quality showed the correspondence of the practical results with the theoretical results, confirming to the reliability of the proposed approaches and full working capacity of the developed software product. For implementation of the subsystem of medical images classification, a neuroparadigm of successive geometric transformations model is adapted.
- Organisation(s)
- Department of Accounting, Innovation and Strategy
- External organisation(s)
- Lviv Polytechnic National University, Stepan Gzhytskyi National University of Veterinary Medicine and Biotechnologies Lviv
- Journal
- CEUR Workshop Proceedings
- Volume
- 2488
- Pages
- 234-245
- No. of pages
- 12
- ISSN
- 1613-0073
- Publication date
- 11-2019
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102020 Medical informatics, 102003 Image processing, 305901 Computer-aided diagnosis and therapy
- Keywords
- ASJC Scopus subject areas
- General Computer Science
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/9749dd47-7ef8-4152-a60b-450637570d29