MRI Brain Tumor Detection and Classification Using U-NET CNN

The uncontrollable and irregular development of cells in the human brain can cause the tumors that appears in all age peoples. Brain tumor detection and classification is a significant step for better understanding its approaches. The tumor in the brain can affect the nerve cells, blood vessels and soft tissues. Hence, there is an essential to detect and classify them in the earlier stages. The tumor is not recognized in the starting stage then it attacks the brain means it creates the death. Magnetic Resonance Imaging (MRI) is an imaging methodology in medicine that uses a large magnet and radio waves that helps the radiologist to identify the tumor region. In this research, the proposed U-NET CNN model for brain tumor detection and classification using Kaggle dataset in MRI images. The obtained result represents that the U-NET CNN proposed technique delivers the performance metrics like accuracy, sensitivity and specificity values about 98.67%, 96.72% and 94.86% respectively which ensures accurate tumor detection and classification compared with other models.