Development of Machine Learning and Medical Enabled Multimodal for Segmentation and Classification of Brain Tumor...

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.