ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2023-23-4-776-785ntv-209Research ArticleИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ И КОГНИТИВНЫЕ ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИARTIFICIAL INTELLIGENCE AND COGNITIVE INFORMATION TECHNOLOGIESСегментация опухоли головного мозга на магнитно-резонансной томографии с использованием нечеткого деформируемого слияния и алгоритма Dolphin-SCABrain tumour segmentation in MRI using fuzzy deformable fusion model with Dolphin-SCAhttps://orcid.org/0000-0003-4411-3878ТиплеА. Х.TipleA. H.

Хемант Типле Анджали — доцент

Колхапур, Махараштра, 416004

Anjali Hemant Tiple — Associate Professor

Kolhapur, Maharashtra, 416004

anjalitiple6@gmail.comhttps://orcid.org/0000-0002-1882-8901КакадеА. Б.KakadeA. B.

Какаде Анандрао Б. — PhD, доцент

sc 24171107900

Уран Ислампур, Махараштра, 415414

Anandrao B. Kakade — PhD, Associate Professor

sc 24171107900

Uran Islampur, Maharashtra, 415414

abkakade22@gmail.comУниверситет ШиваджиИндияShivaji UniversityIndiaТехнологический институт РаджарамбапуИндияRajarambapu Institute of TechnologyIndia202315122024234776785Copyright © Типле А.Х., Какаде А.Б., 20242024Типле А.Х., Какаде А.Б.Tiple A.H., Kakade A.B.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/209

Прекращение функционирования мозга человека на небольшой промежуток времени приводит к смерти. Лечение нарушений головного мозга должно проводиться на ранней стадии и до появления клинических симптомов. Опухоль головного мозга является одним из самых серьезных заболеваний. Развитие опухоли можно обнаружить с помощью магнитно-резонансной томографии (МРТ). В связи с наличием шумов на изображении МРТ опухоль сложно точно и быстро диагностировать. Одно из решений в диагностике опухолей — использование сегментации изображений головного мозга на МРТ. В работе представлена модель томограммы головного мозга обработанная с помощью нелокальных средств (Non-Local Means, NLM) для уменьшения шума от захваченных необработанных данных. Полученное изображение сегментировано с помощью определения областей интереса (ROI) и деформируемой нечеткой системы слияния. Система слияния сочетала в себе метод нечеткой кластеризации C-средних (Fuzzy C-Means, FCM) и деформируемых систем. Выполнен анализ значений пригодности констант α и β сегментированных изображений моделей, объединенных с использованием алгоритма синус-косинуса на основе эхолокации Dolphin-SCA. Интегрированный вывод алгоритма классифицирован с помощью глубокого классификатора (Convolutional Neural Network, CNN). Проведен анализ и сравнение экспериментальных данных созданной модели с текущими методологиями. Значения показателей эффективности предлагаемой модели для селективности, прецизионности, правильности и ошибок составили 0,90, 0,89, 0,88 и 0,10 соответственно. Таким образом, по сравнению с предыдущими стратегиями, предлагаемый подход превосходит ранее применяемые методы.

It is evident that when the human brain stops functioning for a small period of time, it will lead to death. As a result, dealing with brain disorders should be done early and properly. A brain tumour is one of the most serious brain illnesses. The development of tumours can be detected using Magnetic Resonance Imaging (MRI). However, because an MRI image has loud noise, it can be hard to diagnose a tumour. The diagnosis process is slow, yet illness necessitates prompt and accurate medical attention in order for patients to survive. One of the solutions for tumour diagnosis is to employ MRI brain picture segmentation. In this designed model, MRI of the brain is collected and pre-processed with Non-Local Means (NLM) to reduce noise from captured raw data. This pre-processed image is frst segmented with Region of Interest (ROI) for identifying regions of interest and then with a fusion deformable fuzzy system, which combines fuzzy C-means (FCM) and deformable systems. By analyzing the ftness value of α and β constants, segmented pictures from models are fused using the Dolphin Sine Cosine Algorithm (SCA) method to combine the model results. The integrated output from the algorithm is classifed with the deep Convolutional Neural Network (CNN) classifer. The created model experimental fndings are analyzed and compared to current methodologies. The proposed model performance measures are 0.90, 0.89, 0.88, and 0.10 in terms of selectivity, precision, accuracy and errors. As a result, when compared to previous strategies, the proposed approach outperforms them.

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The authors declare that there are no conflicts of interest present.