Understanding Brain Tumor Detection using SVM

Overview

Brain tumors are uncontrolled tissue growths that can occur in any part of the brain. This can be life-threatening if not treated on time. Early detection of brain tumors can ease medical treatment. Biomedical imaging plays an important role in the early detection of tumors. Magnetic resonance imaging (MRI) is the most effective technique for detecting tumors in the brain. The SVM classifier, an advanced diagnostic modality that helps in tumor recognition, saves valuable medical diagnostic time by automatically diagnosing tumors in a short period.

Here we’ve discussed brain tumor detection using an SVM classifier in brief.

Understanding the SVM classifier:

This classifier is a component of machine learning that allows computers to learn. It is a collection of learning methods that analyze data patterns and are used for classification. More than two SVMs are used in a multi-SVM classifier.

It is used to detect and classify the following types of tumors:

• Gliomas, Metastasis, and other types of tumors
• Astrocytoma

The SVM classifier was used to determine if the growth is normal or abnormal. SVM is a binary classification method that employs two input data classes that have been fixed.

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Classification of tumors:

Brain tumors are classified based on their origin, location, tumor area, and biological characteristics of the tissue.

Various kinds of brain tumors are classified as :

• GLIOMAS: Glioma develops from Glial cells in the brain that serve as support cells.
• METASTASIS: This is a type of secondary tumor. They spread to other parts of the body via the bloodstream.
• ASTOCYTOMA: Slow growing, rarely found a tumor. This can spread to other parts of the nervous system. The borders of such tumors are not well defined in the central nervous system (CNS).

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How tumors can be detected using the SVM classifier:

Brain tumor detection uses SVM Classifier to localize a mass of abnormal cells in a slice of Magnetic Resonance (MR) and segmentation of tumor cells to determine the size of the tumor present in that segmented area. To display the type of tumor, the extracted features of the segmented portion will be trained using an artificial neural network.

The detection of brain tumors is a serious problem in imaging science. In general, the size and type of tumor determine the severity of the disease. A critical step in the goal of brain MRI scan image analysis is to extract the tumor boundary and tumor region.

MATLAB software was used to create a brain tumor component. This is an approach based on software that aims to detect and segment the brain tumor from the normal brain.

The tumor detection can be done through the following steps:

• Image acquisition
• Pre-processing
• Morphological operation
• Segmentation
• Feature extraction and selection
• SVM classifier

If a tumor is detected following this step, then stop the procedure at this stage. And if not, tumor classification is done using multi-SVM.

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