# Convolutional neural networks: applications, challenges and future prospects in brain tumor research

## TL;DR

This paper reviews how convolutional neural networks are used in brain tumor research for diagnosis, classification, and prognosis, highlighting their potential and challenges.

## Contribution

The paper provides a systematic review of CNN applications in brain tumor research and outlines future directions.

## Key findings

- CNNs show strong performance in tumor segmentation and classification.
- They can predict IDH mutation status and differentiate tumor recurrence from pseudo-progression.
- Current challenges include data variability and model generalizability.

## 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.