Abstract
Brain tumors remain among the most fatal diseases of the central nervous system, and early diagnosis is vital for improving patient survival outcomes. However, conventional diagnostic approaches relying on manual interpretation of MRI scans by radiologists often suffer from subjectivity, inefficiency, and a high rate of missed diagnoses. To overcome these limitations, this study introduces a systematically optimized deep learning framework designed to achieve real-time performance while preserving high detection accuracy. Specifically, we propose the A2C2f-Mona module, which enhances local and medium-range feature perception through multi-scale depthwise convolution and residual connections; design the C2PSA-DyT module, which replaces conventional normalization layers with element-wise normalization to enhance training stability and feature distribution consistency; and introduce the CGAFusion module, which integrates high- and low-level features via a channel attention mechanism, effectively improving the detection of tumors with blurred boundaries and small volumes. Experimental evaluations demonstrate that the proposed approach surpasses YOLOv12n across all performance metrics, achieving a precision of 93.8%, recall of 88.0%, and mAP@0.5 of 94.0%. Notably, the recall for pituitary tumors shows the greatest improvement, while the recognition accuracy for gliomas is also significantly enhanced. Overall, the proposed method achieves simultaneous gains in detection accuracy and stability, underscoring its substantial potential for advancing intelligent diagnosis in medical imaging.
Data availability
The dataset analyzed during the current study is publicly available from the Kaggle repository: https://www.kaggle.com/datasets/pkdarabi/medical-image-dataset-brain-tumor-detection41.
References
Ostrom, Q. T. et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the united States in 2014–2018. Neuro-oncology 23. Supplement_3 : iii1–iii105. (2021). https://doi.org/10.1093/neuonc/noab200.
Louis, D. N. et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-oncology 23 (8), 1231–1251 (2021).
Stupp, R. et al. Radiotherapy plus concomitant and adjuvant Temozolomide for glioblastoma. N. Engl. J. Med. 352 (10), 987–996 (2005).
Botros, D. et al. Predictors and impact of postoperative 30-day readmission in glioblastoma. Neurosurgery 91 (3), 477–484 (2022).
Ellingson, B. M. et al. Consensus recommendations for a standardized brain tumor imaging protocol in clinical trials. Neuro-oncology 17(9), 1188–1198 (2015).
Allahem, H. et al. A hybrid model of feature extraction and dimensionality reduction using ViT, PCA, and random forest for Multi-Classification of brain cancer. Diagnostics 15 (11), 1392 (2025).
Bauer, S. et al. A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58 (13), R97 (2013).
Zacharaki, E. I. et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reson. Medicine: Official J. Int. Soc. Magn. Reson. Med. 62 (6), 1609–1618 (2009).
Menze, B. H. et al. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinform. 10 (1), 213 (2009).
Ullah, N. et al. TumorDetNet: A unified deep learning model for brain tumor detection and classification. Plos One. 18 (9), e0291200 (2023).
LeCun, Y., Bengio, Y. & Hinton, G. Deep Learn. Nat. 521.7553 : 436–444. (2015).
Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image. Anal. 42, 60–88 (2017).
Shen, D., Wu, G. & Heung-Il, S. Deep Learn. Med. Image Anal. Annual Rev. Biomedical Eng. 19.1 : 221–248. (2017).
Shawon, M. T. et al. Explainable cost-sensitive deep neural networks for brain tumor detection from brain mri images considering data imbalance. Multimedia Tools Appl. 84(35), 1–28 (2025).
Disci, R., Gurcan, F. & Soylu, A. Advanced brain tumor classification in MR images using transfer learning and pre-trained deep. CNN Models Cancers. 17 (1), 121 (2025).
Ishaq, A. et al. Improved EfficientNet architecture for multi-grade brain tumor detection. Electronics 14 (4), 710 (2025).
Tariq, A. et al. Transforming brain tumor detection empowering multi-class classification with vision Transformers and efficientnetv2. IEEE Access. 13, 63857–63876 (2025).
Arshad, M. et al. RaNet: a residual attention network for accurate prostate segmentation in T2-weighted MRI. Front. Med. 12, 1589707 (2025).
Arshad, M. et al. BioAug-Net: a bioimage sensor-driven attention-augmented segmentation framework with physiological coupling for early prostate cancer detection in T2-weighted MRI. BioData Min. 18 (1), 49 (2025).
Mao, Y. et al. Dilated SE-DenseNet for brain tumor MRI classification. Sci. Rep. 15 (1), 3596 (2025).
Sima, M. W. U. et al. Improving kidney segmentation in pathological images: a multiscale approach to resolve fragmentation and incomplete boundaries. J. King Saud Univ. Comput. Inform. Sci. 37 (5), 71 (2025).
Ioffe, S. & Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. International conference on machine learning. pmlr, (2015).
Ba, J., Lei, J. R. & Kiros and Geoffrey E. Hinton Layer. Normalization arXiv Preprint arXiv :160706450 (2016).
Ulyanov, D., Vedaldi, A. & Lempitsky, V. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016).
Wu, Y. & He, K. Group normalization. Proceedings of the European conference on computer vision (ECCV). (2018).
Luo, P. et al. Switchable normalization for learning-to-normalize deep representation. IEEE Trans. Pattern Anal. Mach. Intell. 43 (2), 712–728 (2019).
Nam, H. Hyo-Eun Kim. Batch-instance normalization for adaptively style-invariant neural networks. Adv. Neural Inf. Process. Syst. 31, 2558–2567 (2018).
Zhang, B. & Sennrich, R. Root mean square layer normalization. Adv. Neural. Inf. Process. Syst. 32, 12360–12371 (2019).
Liu, H. et al. Evolving normalization-activation layers. Adv. Neural. Inf. Process. Syst. 33, 13539–13550 (2020).
Brock, A. et al. High-performance large-scale image recognition without normalization. International conference on machine learning. PMLR, (2021).
Dosovitskiy, A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. ArXiv Preprint ArXiv:2010.11929 (2020).
Liu, Z. et al. Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF international conference on computer vision. (2021).
Wu, H. et al. Autoformer: decomposition Transformers with auto-correlation for long-term series forecasting. Adv. Neural. Inf. Process. Syst. 34, 22419–22430 (2021).
Zhou, H. et al. Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI conference on artificial intelligence. 35(12), (2021).
Petar, V. et al. Graph attention networks. International conference on learning representations. 8, (2018).
Brody, S., Alon, U. & Yahav, E. How attentive are graph attention networks? arXiv preprint arXiv:2105.14491 (2021).
Tian, Y., Ye, Q. & Doermann, D. Yolov12: Attention-centric real-time object detectors. arXiv preprint arXiv:2502.12524 (2025).
Yin, H. et al. Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 5100–5110 (2025).
Zhu, J. et al. Transformers without normalization. Proceedings of the Computer Vision and Pattern Recognition Conference. (2025).
Gu, Y. et al. A conditionally parameterized feature fusion U-Net for Building change detection. Sustainability 16 (21), 9232 (2024).
Darabi, P. K. Medical Image Dataset for Brain Tumor Detection. Kaggle, (2023). www.kaggle.com/datasets/pkdarabi/medical-image-dataset-brain-tumor-detection
Chen, M., Xu, Y., Qin, W. & Li, Y. Tomato ripeness detection method based on FasterNet block and attention mechanism. AIP Adv. 1 June. 15 (6), 065117. https://doi.org/10.1063/5.0280801 (2025).
Sohan, M. & Ram, T. S. and Ch., A Review on YOLOv8 and Its Advancements, Algorithms for intelligent systems, 529–545 (2024). https://doi.org/10.1007/978-981-99-7962-2_39
He, L. H., Zhou, Y. Z., Liu, L., Cao, W. & Ma, J. H. Research on object detection and recognition in remote sensing images based on YOLOv11. Sci. Rep. 15 (1), 14032 (2025).
Khanam, R. & Hussain, M. YOLOv11: an overview of the key architectural enhancements. (2024). arXiv: arXiv:2410.17725.
Chen, M., Chen, T., Lou, Y. & Li, Y. Remote sensing image detection method based on context-aware mechanism and transformer architecture. AIP Adv. 1 July. 15 (7), 075326. https://doi.org/10.1063/5.0283520 (2025).
Muksimova, S. et al. A lightweight attention-driven YOLOv5m model for improved brain tumor detection. Comput. Biol. Med. 188, 109893 (2025).
Sun, L., Zheng, L. & Xin, Y. FALS-YOLO: an efficient and lightweight method for automatic brain tumor detection and segmentation. Sensors 25 (19), 5993 (2025).
Funding
This work was supported by the Guangdong Province Basic and Applied Basic Research Foundation Regional Joint Fund Regional Cultivation Project (Yuehui Joint Fund, Project No: 2023A1515140145), the Science and Technology Innovation Team Project of Huizhou Science and Technology Bureau (Project No: 2023EQ050012), the Strong Foundation of Scientific Research Program of Guangzhou Medical University (Project No: 2024SRP215), and the Huizhou Outstanding Young Scientific and Technological Talents Program (Project No: 2025EQ050018).
Author information
Authors and Affiliations
Contributions
Weidong Ye contributed to the conceptualization, methodology, project administration, and funding acquisition. Zhenlin Chen was responsible for resources, data curation, investigation, validation, and writing-original draft. Xingru Sun carried out formal analysis, supervision, and writing-review and editing. Sijia Chen contributed to software development, visualization, data analysis support, and writing-review and editing.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Ye, W., Chen, Z., Sun, X. et al. High-accuracy brain tumor detection method based on deep learning. Sci Rep (2026). https://doi.org/10.1038/s41598-026-35783-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-35783-0
