MINI REVIEW article
Front. Neurol.
Sec. Artificial Intelligence in Neurology
Convolutional Neural Networks: Applications, Challenges and Future Prospects in Brain Tumor Research
Provisionally accepted
Peng Zhang1*
Zhen Yang2- 1The Second People's Hospital of Hefei, Hefei, China
- 2Second People's Hospital of Hefei, Hefei, China
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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.
Keywords: brain tumor, Convolutional Neural Network, image segmentation, Magnetic resonance, Prognosis prediction
Received: 03 Dec 2025; Accepted: 10 Feb 2026.
Copyright: © 2026 Zhang and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Peng Zhang
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