Priyant Banerjee, Monalisa Hati, Vishwa Jadhav, Rushabh Dorage, Utkarsh Mhatre, Sahil Singh | International Journal of Microwave Engineering and Technology | Vol 11, Issue 01 | pp. 1-7 | ISSN: 2455-0337
Abstract
One of the key factors in detecting early diseases is medical image segmentation. Medical image segmentation can be very complex due to the differences in image quality, noise, and specific anatomical structures of individual patients. A novel approach for a hybrid neural network structure that combines the power of convolutional neural networks, and the transformer-based models is described here for improved accuracy and robustness of medical image segmentation. Unlike traditional CNN-only or transformer-only approaches, our method combines the spatial precision of the CNNs with the global contextual understanding of the transformers. The proposed method introduces the adaptive attention mechanism, and it dynamically balances the feature extraction of local and global, thus optimizing the performance across various medical imaging modalities such as MRI, CT, and ultrasound. We did a mixed-methods evaluation, combining quantitative performance metrics (e.g., Dice coefficient, Intersection over Union) with qualitative insights from radiologists to validate the clinical relevance of the results. The initial experiments show a great improvement in segmentation accuracy, especially in complex cases, like tumor boundary detection and vascular anomalies. The model adjusts to diverse clinical scenarios with real-time feedback from medical professionals incorporated into the training loop, thereby enhancing practical applicability. This work advances the frontiers of AI in medical imaging by presenting a synergistic approach that bridges technical innovation with clinical utility. Future work will focus on generalizing the model over different datasets and integrating the model into clinical workflows to provide support for early diagnosis and personalization of treatment planning.
Keywords: Hybrid Neural Networks, Medical Image Segmentation, Transformers, Early Disease Detection, AI in Healthcare.
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- Kim M, Bae HJ. Data augmentation techniques for deep learning-based medical image analyses. Journal of the Korean Society of Radiology. 2020;81(6):1290-304.
- Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, Wang M. Swin-unet: Unet-like pure transformer for medical image segmentation. InEuropean conference on computer vision 2022 Oct 23 (pp. 205-218). Cham: Springer Nature Switzerland.
- Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y. Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306. 2021 Feb 8.
- Nguyen DT, Pham QD, Nguyen TH, Nguyen PN, Nguyen KN, Bui T, Truong SQ. SEGTRANSVAE: Hybrid CNN-Transformer with Regularization for Medical Image Segmentation.
- Child R, Gray S, Radford A, Sutskever I. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509. 2019 Apr 23.
- Kim HE, Cosa-Linan A, Santhanam N, Jannesari M, Maros ME, Ganslandt T. Transfer learning for medical image classification: a literature review. BMC medical imaging. 2022 Apr 13;22(1):69.
- He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. 770-778).
- He K, Gan C, Li Z, Rekik I, Yin Z, Ji W, Gao Y, Wang Q, Zhang J, Shen D. Transformers in medical image analysis. Intelligent Medicine. 2023 Feb 1;3(1):59-78.
- Poudel S, Lee SW. Deep multi-scale attentional features for medical image segmentation. Applied Soft Computing. 2021 Sep 1;109:107445.
- Gerard SE, Herrmann J, Kaczka DW, Musch G, Fernandez-Bustamante A, Reinhardt JM. Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species. Medical image analysis. 2020 Feb 1;60:101592.
- Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999. 2018 Apr 11.
- Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. InMedical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 2015 (pp. 234-241). Springer international publishing.
- Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. Advances in neural information processing systems. 2017;30.
- Qiu P, Yang J, Kumar S, Ghosh SS, Sotiras A. AgileFormer: spatially agile transformer UNet for medical image segmentation. arXiv preprint arXiv:2404.00122. 2024 Mar 29.
- Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122. 2015 Nov 23.
- Takahashi S, Sakaguchi Y, Kouno N, Takasawa K, Ishizu K, Akagi Y, Aoyama R, Teraya N, Bolatkan A, Shinkai N, Machino H. Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. Journal of Medical Systems. 2024 Sep 12;48(1):84.
- Litjens G, Kooi T, Bejnordi BE, Setio AA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI. A survey on deep learning in medical image analysis. Medical image analysis. 2017 Dec 1;42:60-88.
- Yu X, Wang J, Hong QQ, Teku R, Wang SH, Zhang YD. Transfer learning for medical images analyses: A survey. Neurocomputing. 2022 Jun 7;489:230-54.
- Wang R, Lei T, Cui R, Zhang B, Meng H, Nandi AK. Medical image segmentation using deep learning: A survey. IET image processing. 2022 Apr;16(5):1243-67.
- Xu Y, Quan R, Xu W, Huang Y, Chen X, Liu F. Advances in medical image segmentation: a comprehensive review of traditional, deep learning and hybrid approaches. Bioengineering. 2024 Oct 16;11(10):1034.
- Altaf F, Islam SM, Akhtar N, Janjua NK. Going deep in medical image analysis: concepts, methods, challenges, and future directions. IEEE access. 2019 Jul 16;7:99540-72.
- Liu X, Song L, Liu S, Zhang Y. A review of deep-learning-based medical image segmentation methods. Sustainability. 2021 Jan 25;13(3):1224.
- 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. 2021 Feb 17;44(7):3523-42.
- Rasool M, Ismail NA, Boulila W, Ammar A, Samma H, Yafooz WM, Emara AH. A hybrid deep learning model for brain tumour classification. Entropy. 2022 Jun 8;24(6):799.
How to cite this article
@article{BanerjeeP2025,
author = {Priyant Banerjee and Monalisa Hati and Vishwa Jadhav and Rushabh Dorage and Utkarsh Mhatre and Sahil Singh},
title = {AI-Driven Hybrid Neural Network Architecture for Medical Image Segmentation: A Novel Approach for Early Disease Detection},
journal = {International Journal of Microwave Engineering and Technology},
year = {2025},
volume = {11},
number = {01},
pages = {1--7},
issn = {2455-0337},
url = {https://journalspub.com/publication/ijmet/article=16075}
}