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Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor
L Anand1, Kantilal Pitambar Rane2, Laxmi A Bewoor3
1Department of Networking and Communications, SRM Institute of Science and Technology, Chennai, India.
Abstract:
The improper and excessive growth of brain cells may lead to the formation of a brain tumor. Brain tumors are the major cause of death from cancer. As a direct consequence of this, it is becoming more challenging to identify a treatment that is effective for a specific kind of brain tumor. The brain may be imaged in three dimensions using a standard MRI scan. Its primary function is to examine, identify, diagnose, and classify a variety of neurological conditions. Radiation therapy is employed in the treatment of tumors, and MRI segmentation is used to guide treatment. Because of this, we are able to assess whether or not a piece that was spotted by an MRI is a tumor. Using MRI scans, this study proposes a machine learning and medically assisted multimodal approach to segmenting and classifying brain tumors. MRI pictures contain noise. The geometric mean filter is utilized during picture preprocessing to facilitate the removal of noise. Fuzzy c-means algorithms are responsible for segmenting an image into smaller parts. The identification of a region of interest is facilitated by segmentation. The GLCM Grey-level co-occurrence matrix is utilized in order to carry out the process of dimension reduction. The GLCM algorithm is used to extract features from photographs. The photos are then categorized using various machine learning methods, including SVM, RBF, ANN, and AdaBoost. The performance of the SVM RBF algorithm is superior when it comes to the classification and detection of brain tumors.
Insights
This study introduces a machine learning approach for brain tumor detection using MRI scans. The SVM RBF algorithm demonstrated superior performance in accurately classifying and detecting brain tumors.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Brain tumors are a leading cause of cancer death, necessitating advanced diagnostic tools.
- Magnetic Resonance Imaging (MRI) is crucial for neurological condition diagnosis and treatment planning.
- Accurate brain tumor segmentation and classification are vital for effective treatment strategies.
Purpose of the Study:
- To propose a machine learning and medically assisted multimodal approach for brain tumor segmentation and classification using MRI scans.
- To enhance the accuracy of brain tumor identification and characterization.
- To evaluate the effectiveness of various machine learning algorithms in brain tumor detection.
Main Methods:
- Preprocessing MRI images using a geometric mean filter to reduce noise.
- Image segmentation into regions of interest using Fuzzy C-means algorithms.
- Feature extraction using the Grey-Level Co-occurrence Matrix (GLCM) for dimension reduction.
- Classification of brain tumors using Support Vector Machine (SVM), Radial Basis Function (RBF), Artificial Neural Network (ANN), and AdaBoost algorithms.
Main Results:
- The proposed multimodal approach effectively segments and classifies brain tumors from MRI scans.
- Noise reduction was achieved using the geometric mean filter.
- The Support Vector Machine with Radial Basis Function (SVM RBF) algorithm exhibited superior performance in brain tumor classification and detection.
Conclusions:
- The developed machine learning model, particularly SVM RBF, shows significant promise for accurate and reliable brain tumor detection.
- This approach can aid in early diagnosis and improved treatment planning for brain tumors.
- The integration of image processing techniques and machine learning offers a powerful tool for neuro-oncology research and clinical application.
