Abstract
As one of the most common malignant tumors in the central nervous system, brain tumors can cause neurological dysfunction and functional impairment. The early precise diagnosis, therapeutic efficacy evaluation, and prognosis prediction of brain tumors are of crucial significance for the formulation of treatment plans and the extension of survival periods for patients. In recent years, artificial intelligence (AI) has been applied in numerous biomedical fields, including the identification, diagnosis, and treatment of brain tumors. Deep learning (DL) is such an AI tool, and convolutional neural networks (CNNs) are widely used deep learning methods. With their powerful capabilities in automatic feature extraction and pattern recognition of images, CNNs have demonstrated great potential in the analysis of medical images of brain tumors. This paper systematically reviews the research progress of CNNs in brain tumors (tumor region identification and segmentation, benign and malignant classification, IDH mutation status prediction, and differentiation of pseudo-progression and recurrence), and deeply analyzes the current challenges and future development directions, aiming to provide a cutting-edge reference for neurosurgeons and researchers.