Computer Vision for Brain Tumor Classification: A Novel Approach Based on Zernike Moments

The advancement of machine learning techniques has brought significant progress to the classification of brain tumors, proving essential for early diagnoses and effective treatments. This study focuses on evaluating the performance of feature extractors and classifiers for the binary division of brain tumors. Five extractors were employed: Zernike Moments, DWT (Discrete Wavelet Transform), LBP (Local Binary Pattern), GLCM (Gray-Level Co-occurrence Matrix) and HU Moments. The HU Moments extractor stood out with the shortest average execution time, 13.56 microseconds. Subsequently, the Grid Search technique was employed to identify the best hyperparameters for five classifiers: Random Forest, K-Nearest Neighbors, Support Vector Machine, Multilayer Perceptron, and Naive Bayes. Four evaluation metrics were used, prioritizing precision and F1-Score. The results revealed that the combination of Zernike Moments and KNeighbors achieved a test precision of 100.00%, surpassing the results of previous studies.