A COMPREHENSIVE STUDY ON APPLICATION OF DEEP LEARNING IN BRAIN TUMOR DETECTION

Deep Learning is an area of machine learning which, because of its capability to handle a large quantity of data, has demonstrated amazing achievements in each field, notably in biomedicine. Its potential and abilities were evaluated and utilised with an effective prognosis in the identification of brain tumours with MRI pictures. The diagnosis of MRI images by computer-aided brain tumours includes tumour identification, segmentation and classification. Many types of research have concentrated in recent years on conventional or basic machine learning approaches in the detection of brain tumours. Throughout this overview, we offer a comprehensive assessment of the surveys that have been reported so far and the current approaches for detecting tumours. Our review examines the major processes in deep learning approaches for detecting brain tumours including preprocessing, extraction of features and classification and their performance and limitations. We also explore state-of-the-art neural network models to identify brain tumours through extensive trials with and without data augmentation. This review also discusses existing data sets for brain tumour detection assessments.