Open AccessCOMPUTER VISION MODEL COMPARISON
Author(s) -
Da-yoon Nam,
T. Savina
Publication year - 2021
Publication title -
ķazaķstan-britan tehnikalyķ universitetìnìņ habaršysy
Language(s) - English
Resource type - Journals
eISSN - 2959-8109
pISSN - 1998-6688
DOI - 10.55452/1998-6688-2021-18-3-75-82
Subject(s) - computer science , field (mathematics) , artificial intelligence , machine learning , task (project management) , segmentation , software , artificial neural network , selection (genetic algorithm) , medical diagnosis , engineering , mathematics , systems engineering , pure mathematics , programming language , medicine , pathology
The use of machine learning in the medical field is one of the most difficult and thoroughly unsolved problems. Currently, there are many different algorithms for solving problems in the field of diagnostics an segmentation of biomedical images. Researchers are often faced with the challenge of choosing the best method to apply towards their data. We conducted the empirical research and compared 5 algorithms that able to detect anomalies in the medical images: RCNN, Fast-RCNN, Faster-RCNN, Mask R CNN, U-Net, and Residual Neural Network. The advantages of automatic processing of the medical images are apparent: doctors get a convenient software tool that allows them to diagnose the disease faster and reduce possible errors. The task is to study and then select algorithms for further testing on the actual data. The selection and study of algorithms were based on articles describing the architecture and application of computer vision algorithms.