Brain tumor MRI identification and classification using DWT, PCA and kernel support vector machine

Омар Фарук; Джахидул Ислам; Сакиб Ахмед; Саджиб Хоссейн; Нараян Чандра Натх

doi:10.47813/2782-2818-2024-4-1-0133-0152

Авторы

Омар Фарук https://orcid.org/0000-0002-4901-2753
Джахидул Ислам https://orcid.org/0000-0002-6847-1808
Сакиб Ахмед https://orcid.org/0000-0002-4950-4539
Саджиб Хоссейн https://orcid.org/0000-0003-2590-5095
Нараян Чандра Натх https://orcid.org/0009-0009-9651-747X

DOI:

https://doi.org/10.47813/2782-2818-2024-4-1-0133-0152

Ключевые слова:

Классификация, извлечение признаков, сегментация изображений, предварительная обработка, PNN, Дискретное вейвлет-преобразование, Вероятностная нейронная сеть

Аннотация

Классификация, сегментация и идентификация области инфекции на МРТ-изображениях опухолей головного мозга являются трудоемкими и итеративными процессами. Многочисленные анатомические структуры человеческого тела можно представить с помощью теории обработки изображений. С помощью базовых методов визуализации сложно увидеть аномальную структуру человеческого мозга. Неврологическую структуру человеческого мозга можно различить и уточнить с помощью метода магнитно-резонансной томографии. Подход МРТ использует ряд методов визуализации для оценки и регистрации внутренних особенностей человеческого мозга. В этом исследовании мы сосредоточились на стратегиях удаления шума, извлечении признаков из матрицы совместной встречаемости на уровне серого (GLCM) и сегментации областей опухоли головного мозга на основе дискретного вейвлет-преобразования (DWT) для минимизации сложности и повышения производительности. В свою очередь, это уменьшает любой шум, который мог остаться после сегментации из-за морфологической фильтрации. Сканирование МРТ головного мозга использовалось для проверки точности классификации и местоположения опухоли с использованием вероятностных классификаторов нейронных сетей. Точность классификатора и определение положения были проверены с помощью МРТ головного мозга. Эффективность предложенного подхода подтверждается экспериментальными результатами, которые показали, что нормальные и больные ткани можно отличить друг от друга на МРТ головного мозга с точностью около 100%.

Биографии авторов

Омар Фарук

Omar Faruq, PhD Research Fellow in Electronic and Electrical Engineering at Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. He is an Assistant Professor of Computer Science and Engineering at BGIFT Institute of Science and Technology, Dhaka, Bangladesh. He received his Post-Graduate degree from the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, China. He also graduated from Electrical and Electronic Engineering at Daffodil International University, Dhaka, Bangladesh. He engaged in research in Machine Learning, Deep Learning, Artificial Intelligence, Image and Signal Processing, Computer Vision, Free-Space Optical Communication, Optical Fiber Communication, Wireless Communications and Networks, etc.

Джахидул Ислам

Islam Md. Jahidul, is A post-graduate student of Software Engineering at Northeastern University, China. He graduated from school of software engineering at Chongqing University of Posts and Telecommunications, China. His interested field of research is computer vision, image processing, signal processing, artificial intelligence, machine learning, deep learning, neural networks, etc.

Сакиб Ахмед

Ahmed Md. Sakib, Electrical and Electronic Engineering at Green University Bangladesh, Bangladesh. His interested field of research is image processing, bio-medical, bio-informatics, etc.

Саджиб Хоссейн

Md. Sajib Hossain, Electrical and Electronic Engineering at Green University Bangladesh, Bangladesh. His interested field of research is image processing, bio-medical, bio-informatics, etc.

Нараян Чандра Натх

Nath Narayan Chandra, A post-graduate Master’s program: Control, Microsystems, Microelectronics at University of Bremen, Bremen, Germany. Also received his Post-Graduate degree from School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, China. His interested field of research is Control system, microelectronics, computer vision, image processing, signal processing, artificial intelligence, machine learning, deep learning, neural networks, etc.

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