“Deep Learning-Based Detection of Pediatric Brain Tumor Presence on MRI”
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ID: 322256
2026
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Abstract
Abstract Introduction Pediatric cancer imposes a significant global burden, with ∼400,000 new cases annually [WHO, 2025]. Magnetic resonance imaging (MRI) is a fundamental technique for timely diagnosis; however, interpretation can be subjective. In this context, convolutional neural networks (CNNs) have emerged as a promising tool for early and accurate detection of brain tumors in pediatric patients. Objective To develop and validate a CNN model for tumor detection tasks (tumor vs. non-tumor) of pediatric brain MRI images using T1-weighted imaging. Methodology T1-weighted brain MRI images from 285 pediatric patients were included (140 with confirmed tumors and 145 controls). A CNN architecture with convolutional layers, max-pooling, and dropout regularization was implemented. The model was trained and evaluated using cross-validation, employing the following metrics: accuracy, precision, recall, and F1-score. Model performance was compared across training configurations of 10–50 and 100 epochs to determine the optimal setup. Results The model trained for 40 epochs achieved the best overall performance, with a precision of 99%, a recall of 100%, and an F1-score of 99%. These metrics demonstrate excellent tumor detection while eliminating false negatives, a crucial aspect in pediatric oncology. Conclusion The CNN demonstrates strong potential as a diagnostic aid for detection brain tumors using pediatric MRI images. Its integration into clinical environments could contribute to more objective interpretation, optimize diagnostic workflows, and provide imaging follow-up before and after treatment.
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openalex_W7170734967
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| Authors | Estefania Reyes Soto, Silvia Hidalgo Tobón, Dulce Judith Almanza Aranda, Bertha Lilia Romero Baizabal, Benito de Celis Alonso, Samuel Torres García, Jorge Villalpando Espinoza |
| Journal | Neuro-Oncology Advances |
| Year | 2026 |
| DOI |
10.1093/noajnl/vdag187
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| URL | |
| Keywords | Keywords not found |
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