Medical Image Segmentation Based on Hybrid Deep Learning Techniques

ABSTRACT: Medical image segmentation is an integral component of computer-aided treatment planning and diagnosis, enabling accurate analysis of abnormalities and anatomical structures. In this paper, a novel U-Net and transformer encoder-based hybrid deep learning architecture is developed to advance precision and speed in medical image segmentation. The U-Net architecture, for its remarkable capability to capture local details, is integrated in synergy with a transformer encoder, whose power is in capturing data's long-range dependencies. The synergy between such capabilities makes the resultant hybrid architecture robust for segmentation in diverse medical imaging modalities. The approach is rigorously compared on publicly available datasets, including brain MRI, Chest X-Rays, and ISIC, and is shown to have better Dice coefficients than current state-of-the-art. The synergy between U-Net and transformer components, in addition to better segmentation, is shown to result in computational savings, thus becoming efficient for clinical application. The result demonstrates the capability of such a hybrid architecture to advance diagnostic precision and contribute to enhanced treatment planning in clinical settings. The work is in alignment with growing literature on using advanced deep learning to overcome challenging medical image analysis problems, ultimately leading to enhanced clinical outcomes through accurate and reliable tools for segmentation.