Slice-Group-Level Brain Tumor Classification Using CRNN-GRU
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Abstract
Interpreting brain MRI for tumor diagnosis is time-consuming and varies between raters, and single-slice decisions can be fragile. This paper compares two classification pipelines on the Brain Tumor Classification (MRI) Dataset from Kaggle (glioma, meningioma, pituitary, no-tumor) of T1-contrast-enhanced images. The first is a per-slice CNN. The second is a per-group CRNN-GRU that reads an ordered sequence of ten slices with a bidirectional GRU and inter-slice attention. Because the dataset provides no patient identifiers, the analysis unit is a surrogate slice group, and the splits are group-disjoint by construction; all models are compared at the group-level. The per-slice CNN reached 80.0% slice-level accuracy. Aggregated to the group-level, the same CNN reached 92.68% by majority voting and 95.12% by mean-probability pooling. The CRNN-GRU reached 90.2% in a single pass, and 87.8% with test-time augmentation. No pairwise difference is statistically significant (exact McNemar, all p >= 0.25). The results do not show that sequential modelling and inter-slice attention improve on simple probability aggregation on this dataset, and the simplest configuration was the most accurate. A content-level audit found a small number of images duplicated across partitions, so all figures should be read as upper bounds. These results describe internal performance on one dataset under group-disjoint evaluation with a content-level leakage audit and do not establish clinical generalization.
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How to Cite
Almadani, M. Z., Aziz, M. A., & Muda, T. Z. T. (2026). Slice-Group-Level Brain Tumor Classification Using CRNN-GRU. International Journal of Basic and Applied Science, 15(2), 66–85. https://doi.org/10.35335/ijobas.v15i2.944
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[22] Z. N. K. Swati et al., “Brain tumor classification for MR images using transfer learning and fine-tuning,” Computerized Medical Imaging and Graphics, vol. 75, pp. 34–46, 2019, https://doi.org/10.1016/j.compmedimag.2019.05.001.
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[24] M. M. Ahmed et al., “Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh,” Sci. Rep., vol. 14, no. 1, p. 22797, 2024, https://doi.org/10.1038/s41598-024-71893-3.
[25] A. Priya and V. Vasudevan, “Brain tumor classification and detection via hybrid alexnet-gru based on deep learning,” Biomed. Signal Process. Control, vol. 89, p. 105716, 2024, https://doi.org/10.1016/j.bspc.2023.105716.
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