Automated Medical Image Segmentation Using Hybrid Transfer Learning Attention Network
International Journal of Medical Toxicology and Forensic Medicine,
Vol. 16 (2026),
1 Dey 2026
,
Page 1-12
https://doi.org/10.22037/ijmtfm.v16.53261
Abstract
Background: Diagnosis of brain tumors using magnetic resonance imaging (MRI) is essential in clinical diagnosis and treatment planning since it is accurate and allows for early diagnosis. However, there is a shortage of annotated medical data and inter-class similarity among tumor types, posing a challenge for automated classification systems. The proposed research is a Hybrid Transfer Learning Attention Network (HTLA-Net) for multi-class brain tumor classification, based on pre-trained convolutional neural networks and stage-by-stage fine-tuning.
Methods: The proposed architecture of HTLA-Net is based on a combination of backbone-specific pre-processing, feature extraction, and selective layer modification to expand learning while maintaining consistent generalization. The authors evaluated four state-of-the-art architectures, i.e., EfficientNetB0, ResNet50, MobileNetV2, and DenseNet121, on a publicly available four-class MRI dataset, comprising glioma, meningioma, pituitary macroadenoma, and healthy cases.
Results: The experiment shows that DenseNet121 achieved the best overall performance, with a test accuracy of 85.96, a macro F1-score of 0.8557, a specificity of 0.9532, and an ROC-AUC of 0.9597, attributable to its strong ability to distinguish between classes and its robust diagnostic performance.
Conclusion: The proposed hybrid training plan is an effective way to mitigate overfitting and improve calibration compared to simpler architectures. These findings affirm the suitability and efficiency of deep models that rely on transfer learning for reliable computer-based diagnosis of brain tumors in settings with limited medical practice and data.
- Brain tumor classification, Magnetic resonance imaging (MRI), Transfer learning, Deep learning, Convolutional neural networks (CNN), EfficientNet, ResNet, DenseNet, Medical image analysis, Computer-aided diagnosis, ROC–AUC, Sensitivity, Specificity
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References
[1] Çevik N, Çevik T, Osman O, Alsubai S, Rasheed J. Advancing patient care with AI: a unified framework for medical image segmentation using transfer learning and hybrid feature extraction. Frontiers in Medicine. 2025;12. [DOI: 10.3389/fmed.2025.1589587]
[2] Messaoudi H, Belaid A, Salem D, Conze P. Cross-dimensional transfer learning in medical image segmentation with deep learning. Medical Image Analysis. 2023;88:102868. [DOI: 10.1016/j.media.2023.102868]
[3] Kora P, Ooi C, Faust O, Raghavendra U, Gudigar A, Chan W, et al. Transfer learning techniques for medical image analysis: A review. Biocybernetics and Biomedical Engineering. 2021. [DOI: 10.1016/j.bbe.2021.11.004]
[4] Lei T, Wang R, Wan Y, Du X, Meng H, Nandi A. Medical Image Segmentation Using Deep Learning: A Survey. ArXiv. 2020;abs/2009.13120. [DOI: 10.1049/ipr2.12419]
[5] Zhang L, Wang X, Yang D, Sanford T, Harmon S, Turkbey B, et al. Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation. IEEE Transactions on Medical Imaging. 2020;39:2531-40. [DOI: 10.1109/tmi.2020.2973595]
[6] Lu L, Liang M. Deep learning-driven medical image analysis for computational material science applications. Frontiers in Materials. 2025. [DOI: 10.3389/fmats.2025.1583615]
[7] Haque I, Neubert J. Deep learning approaches to biomedical image segmentation. Informatics in Medicine Unlocked. 2020;18:100297. [DOI: 10.1016/j.imu.2020.100297]
[8] Verma A, Yadav A. Brain tumor segmentation with deep learning: Current approaches and future perspectives. Journal of Neuroscience Methods. 2025;418. [DOI: 10.1016/j.jneumeth.2025.110424]
[9] Kourounis G, Elmahmudi A, Thomson B, Nandi R, Tingle S, Glover E, et al. Deep learning for automated boundary detection and segmentation in organ donation photography. Innovative Surgical Sciences. 2024;10:131-41. [DOI: 10.1515/iss-2024-0022]
[10] Xia Q, Zheng H, Zou H, Luo D, Tang H, Li L, et al. A comprehensive review of deep learning for medical image segmentation. Neurocomputing. 2024;613:128740. [DOI: 10.1016/j.neucom.2024.128740]
[11] Isensee F, Jaeger P, Kohl S, Petersen J, Maier-Hein K. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods. 2020;18:203-11. [DOI: 10.1038/s41592-020-01008-z]
[12] Minaee S, Boykov Y, Porikli F, Plaza A, Kehtarnavaz N, Terzopoulos D. Image Segmentation Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2020;44:3523-42. [DOI: 10.1109/tpami.2021.3059968]
[13] Wang J, Zhu H, Wang S, Zhang Y. A Review of Deep Learning on Medical Image Analysis. Mobile Networks and Applications. 2020;26:351-380. [DOI: 10.1007/s11036-020-01672-7]
[14] Jiang X, Hu Z, Wang S, Zhang Y. Deep Learning for Medical Image-Based Cancer Diagnosis. Cancers. 2023;15. [DOI: 10.3390/cancers15143608]
[15] Liu X, Song L, Liu S, Zhang Y. A Review of Deep-Learning-Based Medical Image Segmentation Methods. Sustainability. 2021;13(3):1224. [DOI: 10.3390/su13031224]
[16] Wang S, Li C, Wang R, Liu Z, Wang M, Tan H, et al. Annotation-efficient deep learning for automatic medical image segmentation. Nature Communications. 2020;12. [DOI: 10.1038/s41467-021-26216-9]
[17] Zhou W. Medical Image Information Segmentation Algorithm based on Deep Learning. 2025 International Conference on Intelligent Computing and Knowledge Extraction (ICICKE). 2025:1-6. [DOI: 10.1109/icicke65317.2025.11136622]
[18] Jamil N, Khan AA. Multi-Class Brain Tumor MRI Dataset: Glioma, Healthy Brain, Meningioma, and Pituitary Macroadenoma. Mendeley Data. 2025;V2. [DOI: 10.17632/82mtzd8x72.2]
[19] Osmani N, Rezayi S, Esmaeeli E, Karimi A. Transfer Learning from Non-Medical Images to Medical Images Using Deep Learning Algorithms. Frontiers in Health Informatics. 2024. [DOI: 10.30699/fhi.v13i0.549]
[20] Fu Y, Lei Y, Wang T, Curran W, Liu T, Yang X. A review of deep learning based methods for medical image multi-organ segmentation. Physica Medica. 2021;85:107-22. [DOI: 10.1016/j.ejmp.2021.05.003]
[21] Renard F, Guedria S, Palma N, Vuillerme N. Variability and reproducibility in deep learning for medical image segmentation. Scientific Reports. 2020;10. [DOI: 10.1038/s41598-020-69920-0]
[22] Singh M, Kr V, Gupta S, HoD D. Deep Learning-Driven Knowledge Transfer for Automated Medical Image Segmentation. 2025 3rd International Conference on Disruptive Technologies (ICDT). 2025:76-82. [DOI: 10.1109/icdt63985.2025.10986641]
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