Brain Tumor Detection and Classification Using Hybrid VGG-XGB model

Abstract

To avoid potential fatalities, it is vital to diagnose brain tumors at its earlier stage itself. Tumors vary widely in shape, size, and location and this makes accurate segmentation and classification difficult even with significant efforts in this field. Therefore, a hybrid method for classifying brain tumors called the hybrid VGG-XGBoost (hybrid VGG-XGB) model is proposed in this work. The K-means technique is used to extract significant characteristics from brain tumor images after they have first been improved using contrast-limited adaptive histogram equalization (CLAHE). Then, for extracting the high-level features segmented regions, VGG-19 model has been used and for classification process, XGBoost supervised learning technique has been utilized. As evidenced by criteria like precision, recall, F1-score, kappa score, and the confusion matrix, our novel approach outperformed baseline models and produced excellent results with a 98% accuracy rate. The suggested methodology for brain cancer diagnosis in healthcare systems is recommended by us, based on highly predictive outcomes