Brain Tumor Classification and Detection Based DL Models: A Systematic Review

Abstract

In recent years, the realms of computer vision and deep learning have ushered in transformative changes across various domains. Among these, deep learning stands out for its remarkable capacity to handle vast datasets, revolutionizing numerous fields, including the biomedical sector. In particular, its prowess has been harnessed in the realm of brain tumor identification through MRI scans, yielding impressive results. This research project is dedicated to conducting an exhaustive exploration of existing endeavors in the domain of brain tumor identification and classification via MRI scans. This endeavor is poised to be of profound value to researchers looking to leverage their deep learning expertise in the realm of brain tumor detection and categorization. The initial phase involves an overview of prior studies that have employed deep learning for categorising and detecting brain tumors. Subsequently, a meticulous analysis of deep learning studies proposed in research publications spanning (2019 to 2022) is presented in tabular form. The conclusion section comprehensively assesses the merits and demerits inherent in deep neural networks. The insights gleaned from this study promise to equip future researchers with a holistic perspective on current research trends and a nuanced understanding of the effectiveness of diverse deep learning methodologies. It is our fervent belief that this research will significantly advance the understanding of brain tumors and their detection methodologies.

Keywords

• Cancer
• tumor classification
• features extraction
• tumor detection
• tumor segmentation