Authors - Rakesh Babu B, Rajesh V, Syed Inthiyaz, Srinivasa Rao K, Sri Sravan V Abstract - Brain tumours are life-threatening disorders with significant fatality rates. Patients have a higher chance of survival when brain tumours are diagnosed early and treated more effectively. Therefore, for the purpose of better and boost the early identification of brain tumours, computerized segmentation as well as classification techniques are needed. It is possible to safely and promptly detect tumours using brain scans such as computed tomography (CT), magnetic resonance imaging (MRI) and other techniques. Revolutionary changes have occurred in many different disciplines as a result of recent developments in artificial intelligence (AI). AI models are becoming essential tools for interpreting images in bio medical field. Deep learning is one of these that signifies extraordinary capacity to deal with enormous data collection, revolutionizing numerous fields in the biomedical profession. This article evaluates a state-of-the-art AI based segmentation and classification system and discovers major classes for brain tumours. The potent learning capability and effectiveness of AI approaches have been assessed. Convolutional Neural Network (CNN) is one of the AI subfields that has demonstrated remarkable performance in analysing medical imagery. Consequently, the processing of medical imagery, particularly brain MRI images, was the main emphasis of this review paper, which also examined different deep learning model architectures in addition to CNN.
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