Deep Learning–Based Convolutional Neural Networks for Brain Tumor Detection on CT scans: A Systematic Review

REVIEW
Sara Ali, Meaad Elbashir, Almohannad Hamzi, Imad Akour, Anwar Sheikhain, Raed Al-Shahri, Turkey Refaee, Ali Abdelrazig, Awatif M. Omer

 

For citation: Ali S, Elbashir M, Hamzi A, Akour I, Sheikhain A, Al-Shahri R, Refaee T, Abdelrazig A, Omer AM. Deep Learning– Based Convolutional Neural Networks for Brain Tumor Detection on CT scans: A Systematic Review. International Journal of Biomedicine. 2026;16(3):306-313. doi:10.21103/Article16(3)_RA2

Originally published September 5, 2026

Abstract: 

This systematic review evaluates the efficacy and diagnostic performance of deep learning algorithms for detecting brain tumors from computed tomography (CT) data. To assess the diagnostic performance of convolutional neural network (CNN)-based models, a descriptive analysis of metrics such as accuracy, specificity, and precision was conducted. Results from nine recent studies demonstrated high diagnostic performance for deep learning methods. Most models achieved accuracy, precision, and specificity exceeding 95% in the differential diagnosis of neoplastic versus non-neoplastic lesions on imaging. These findings highlight the potential of CNNs to facilitate the early diagnosis of brain tumors, particularly in emergency settings where CT serves as the first-line imaging modality.

The analyzed studies employed a wide range of CNN architectures—from pre-trained models to custom-designed networks—yielding consistent and reliable results. However, several limitations were identified, including small sample sizes, reliance on retrospective data, insufficient external validation, and variations in approaches to interpreting results. Despite these limitations, the consistency of findings across independent studies confirms the reliability and practical potential of CNN-based methods for brain tumor detection in CT imaging. Future research should focus on prospective study designs, larger multicenter datasets, standardized reporting, and enhanced model interpretability to ensure safe and effective integration into clinical practice.

Keywords: 
deep learning, convolutional neural network, brain tumour, computed tomography
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Received April 21, 2026.
Accepted June 11, 2026.
© 2026 The Author(s). International Journal of Biomedicine is published by IMRDC. This is an open access article under the CC BY-NC-ND 4.0 license.