Neenu Joseph,
Akhil Musthafa,
Namitha M.R,
Misbah Latheef,
- Assistant Professor, Albertian Institute of Science and Technology, Ernakulam, India
- Student, Albertian Institute of Science and Technology, Ernakulam, India
- Student, Albertian Institute of Science and Technology, Ernakulam, India
- Student, Albertian Institute of Science and Technology, Ernakulam, India
Abstract
The paper discusses the importance of detecting and classifying brain tumors via MRI for effective treatment. It proposes a framework utilizing the Fuzzy C-means clustering algorithm for segmentation, demonstrating improved performance through real dataset validation. The model is trained on a large, annotated MRI dataset to identify and classify different tumor types, enabling machine learning-based classification into benign and malignant tumors. The MATLAB-based solution automates brain tumor feature extraction, aiding healthcare professionals in neuro imaging and diagnostics. Our suggested approach starts with pre-processing MRI pictures to improve contrast and lower noise, which will lead to more accurate segmentation results. We use machine learning approaches to categorize tumors into benign and malignant categories based on extracted features, leveraging a large, annotated MRI dataset. Extensive studies on real datasets verified the results, which show improved performance measures. This MATLAB-based system helps medical professionals with neuroimaging and diagnostics in addition to automating the tumor detection process, opening the door to more effective and dependable treatment approaches. The framework involves pre-processing MRI images to enhance contrast and remove noise, followed by segmentation to isolate tumor regions, presenting a comprehensive approach for automated brain tumor detection and classification using advanced machine learning and image processing techniques.
Keywords: Feature extraction, Machine Learning, MRI Images, clustering algorithm, Image classification
[This article belongs to Journal of Microwave Engineering and Technologies(jomet)]
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Journal of Microwave Engineering and Technologies
| Volume | 11 |
| Issue | 02 |
| Received | July 6, 2024 |
| Accepted | July 16, 2024 |
| Published | August 14, 2024 |