MATLAB code : Brain tumor classification in MRI images using SVM

The MATLAB code performs brain tumor classification on MRI images using the SVM algorithm. It focuses on two main steps: feature extraction and model building/classification.

Step 1: Extraction of Features

1. Data Preparation

2. Feature Extraction Functions

uses wavelet coefficients and Gabor filter properties .

3. Feature Concatenation

– Merges wavelet moments, Gabor features, and class labels (cancerous or non-cancerous) into a dataset (`dataset`).

– Saves the dataset for future reference.

Step 2: Modelling and Classification

1. Model Training

2. Test Image Classification

Key Points:

– The code utilizes wavelet and Gabor transforms for an effective feature extraction, thus considering both the intensity and texture details.

– The SVM classifier propose a robust technique for binary classification.

– The test image which can be chosen by the user allows for real-world use.

File contents:

• database – mat file 
• m file
• train and test images database
• docx file: including complete explanations of MATLAB code and its methods.