Abstract: The World Health Organisation (WHO) identifies brain tumours as one of the leading causes of death in the world. This disease is challenging to identify because of its complexity and cunning character. Because of the high risk of clinical occurrences, persistent brain tumour illness is a severe public health issue worldwide. Despite the general consensus that persistent brain tumour disease has considerable interactions with elevated risks of vascular events, end-stage excretory organ disease, and all-cause mortality, there is still inadequate information on individuals. Deep learning (DL), a branch of machine learning, has recently shown impressive results, particularly in tasks like classification and segmentation. Imaging can be done in various ways to look for brain tumours. Magnetic Resonance Imaging MRI is widely utilized because it produces high-quality images without harmful ionizing radiation. MRI enables the early diagnosis and evaluation of brain tumours as a preventive medical measure. Brain tumour diagnosis is aided by MRI, which provides thorough information on human-sensitive tissue. The Convolutional Neural Network (CNN) is a popularly used method and sought-after model for classification in modern times. Like the human brain, the CNN-based expert system's input, neurons, hidden layers, and output are all interconnected. The study focuses on developing and optimizing deep learning models to handle the complexity and heterogeneity of brain tumours. CNNs are commonly employed for their ability to automatically learn discriminative features from medical images, particularly MRI scans. These models leverage large datasets to understand representations that capture the subtle variations and distinctive patterns indicative of brain tumours.

Brain tumour, Deep Learning, Machine Learning, Convolution Neural Networks (CNN), MRI