CNN-based Learning Approach for Detecting Brain Tumors from Magnetic Resonance Images (MRIs)

Abstract

Abstract: The majority of malignancies that originate in the CNS occur in the brain. Annually, almost 11,700 people are told they have a brain tumour. About 34% of men and 36% of women survive 5 years after being diagnosed with a brain or central nervous system tumour. Benign tumours, malignant tumours, pituitary tumours, and so on are all different types of brain tumours. A patient's life expectancy can be increased via careful diagnosis, treatment, and planning. Magnetic resonance imaging (MRI) is the gold standard for diagnosing tumours in the brain. Scanning generates a massive amount of data in the form of images. A radiologist is the professional who looks at these scans. Manual examinations are unreliable because brain tumours are complex. ML and AI-based classification approaches outperform human-based methods. Thus, a global detection and classification system using deep learning techniques like CNN, ANN, and TL will help clinicians worldwide