Automated Brain Tumor Segmentation and Classification Through MRI Images

Abstract

The brain tumor is considered a hazardous infection and may cause death. Therefore, early detection of brain tumors can improve the survival rate. This paper presents convolutional neural network method for segmentation and classification of brain tumors using magnetic resonance images. The proposed method has achieved an accuracy of 98.97%, specificity of 97.35%, sensitivity of 97%, precision of 97.90%, and F1-score of 96% for brain tumor segmentation. While on brain tumor classification, the proposed method shows accuracy 98.25%, sensitivity 98%, specificity 98.5%, precision 97.21% and F1-score 97%. The BRATS 2020 dataset has been utilized for training and testing the proposed method for brain tumor segmentation and classification.