AI-Driven Hybrid Neural Network Architecture for Medical Image Segmentation: A Novel Approach for Early Disease Detection

Volume: 11 | Issue: 01 | Year 2025 | Subscription
International Journal of Microwave Engineering and Technology
Received Date: 03/05/2025
Acceptance Date: 04/04/2025
Published On: 2025-04-10
First Page: 1
Last Page: 7

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By: Priyant Banerjee, Monalisa Hati, Vishwa Jadhav, Rushabh Dorage, Utkarsh Mhatre, and Sahil Singh

1- Research Scholar, Department of Computer Science and Engineering, Amity University Mumbai, India
2- Assistant Professor, Department of Computer Science and Engineering, Amity University Mumbai, India
3- Research Scholar, Department of Computer Science and Engineering, Amity University Mumbai, India
4- Research Scholar, Department of Computer Science and Engineering, Amity University Mumbai, India
5- Research Scholar, Department of Computer Science and Engineering, TPCT’S College of Engineering Osmanabad, India
6- Research Scholar, Department of Computer Science and Engineering, TPCT’S College of Engineering Osmanabad, India

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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Citation:

How to cite this article: Priyant Banerjee, Monalisa Hati, Vishwa Jadhav, Rushabh Dorage, Utkarsh Mhatre, and Sahil Singh, AI-Driven Hybrid Neural Network Architecture for Medical Image Segmentation: A Novel Approach for Early Disease Detection. International Journal of Microwave Engineering and Technology. 2025; 11(01): 1-7p.

How to cite this URL: Priyant Banerjee, Monalisa Hati, Vishwa Jadhav, Rushabh Dorage, Utkarsh Mhatre, and Sahil Singh, AI-Driven Hybrid Neural Network Architecture for Medical Image Segmentation: A Novel Approach for Early Disease Detection. International Journal of Microwave Engineering and Technology. 2025; 11(01): 1-7p. Available from:https://journalspub.com/publication/ijmet/article=16075

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