Computer vision based efficient segmentation and classification of multi brain tumor using computed tomography images.

This study aims to highlight the effectiveness of computer vision (CV) techniques in classifying brain tumors using a comprehensive dataset consisting of computed tomography (CT) scans. The proposed framework comprises six types of brain tumors, including benign tumors (Meningioma, Schwannoma, and Neurofibromatosis) and malignant tumors (Glioma, Chondrosarcoma, and Chordoma). The acquired images underwent pre-processing steps to enhance the dataset's quality, including noise reduction through median and Gaussian filters and region of interest (ROIs) extraction using an automated binary threshold-based fuzzy c-means segmentation (ABTFCS) approach.