Multimodal Biomedical Image Segmentation using Deep Learning Method

Abstract :

Biomedical image segmentation is essential in medical diagnostics, enabling precise delineation of anatomical structures across multiple imaging modalities. Recent advances in deep learning (DL) have transformed this field by reducing dependence on handcrafted features. However, conventional architectures such as U-Net face challenges when processing complex multimodal datasets. This study introduces an enhanced DL-based framework for multimodal biomedical image segmentation that incorporates hierarchical feature extraction and multi-scale processing to improve segmentation performance. The proposed model is evaluated on diverse biomedical datasets, demonstrating superior results compared to traditional architectures. Experimental findings reveal notable improvements in difficult segmentation scenarios, particularly in cases where conventional approaches underperform. The method delivers more accurate boundary detection and robust segmentation across varying resolutions and contrast levels. By harnessing DL advancements, this work contributes to more effective automated medical image analysis, supporting improved accuracy and reliability in clinical decision-making.