MRI Brain Tumor Classification Using Self-Organizing Maps (SOM)

In this article, we will conduct an in-depth exploration of MRI brain tumor classification, specifically focusing on the application of Self-Organizing Maps (SOM) for this challenging task. The implementation typically involves preprocessing MRI scans using Python's scikit-image or OpenCV libraries for noise reduction and normalization. We begin by introducing the standard MRI image processing pipeline and the specific challenges in brain tumor classification, such as dealing with high-dimensional feature spaces and class imbalance. Next, we examine the fundamental principles of the SOM algorithm, explaining why it serves as an effective solution for unsupervised pattern recognition in medical imaging. The Kohonen network implementation involves creating a 2D grid of neurons where each neuron learns to represent prototype vectors through competitive learning. Key functions include calculating Euclidean distance between input vectors and weight vectors, and updating weights using neighborhood functions like Gaussian or Mexican-hat kernels. We then provide detailed experimental methodology, including feature extraction techniques (such as texture descriptors using GLCM or wavelet transforms) and SOM training parameters (learning rate decay, neighborhood size reduction). The experimental results analysis discusses quantization error, topographic error, and classification accuracy metrics, with visualization of the SOM U-matrix showing cluster formation corresponding to different tumor types. Finally, we summarize the main contributions of this research, including the development of an automated SOM-based classification system that can handle non-linear data relationships, and discuss future research directions such as integrating SOM with deep learning architectures or extending the approach to multi-modal MRI data classification.