EARLY DETECTION OF UTERINE FIBROIDS ENHANCED BY A DUAL-SCALE TRANSFORMER-CNN HYBRID SEGMENTATION NETWORK (DSTCH-NET)

Accurate and early segmentation of uterine fibroids is necessary for quick evaluation, planning of treatment, and better results for patients. However, current segmentation techniques often struggle to achieve reliable results due to the complex morphology, irregular boundaries, low contrast, and noise present in MRI and ultrasound images. This research proposes a solution to these challenges by proposing an innovative Dual-Scale Transformer-CNN Hybrid Segmentation Network (DSTCH-Net) that uses the strengths of both transformer and convolutional architectures. The Fine-Scale Transformer Branch captures the global context and long-range dependencies, while the Coarse-Scale CNN Branch focuses on detailed local feature extraction and texture analysis. A Contextual Cross-Attention Fusion Module (CCAFM) integrates essential features from both branches, and an Adaptive Boundary Refinement Module (ABRM) further enhances edge precision. Experimental analysis shows that DSTCH-Net can be evaluated at 99.2% segmentation accuracy at a loss of 0.07, which is better than the state-of-the-art models. These findings confirm that the suggested method can be used to effectively overcome the constraints of traditional methods and offer a strong framework for accurate and early detection of uterine fibroids in clinical practice