Enhancing Breast Cancer Collagen Segmentation with Improved Swim Transformation Algorithm

The precise segmentation of collagen fibres in breast tissue is essential for an efficient diagnostic and treatment strategy because breast cancer is still a major worldwide health concern. By better separating fibers from the background, the Swim Transformation (SWT) algorithm has demonstrated potential to improve collagen fiber segmentation. Unfortunately, the resilience and accuracy of the original SWT method are limited because to its susceptibility to noise and distortions. This work suggests an updated version of the SWT algorithm that includes multiple improvements to maximize collagen segmentation in breast cancer tissues, thereby addressing these constraints. The novel method improves the detection of collagen fibers by introducing a noise reduction pre-processing step and adaptive thresholding. It also incorporates an extra post-processing phase to eliminate segmentation mistakes and improve the overall outcomes. Using a dataset of microscopic pictures of breast cancer tissue, the suggested method's effectiveness was assessed and contrasted with that of the original SWT algorithm and other cutting-edge segmentation techniques. The enhanced SWT algorithm achieves greater segmentation accuracy, sensitivity, and precision, according to experimental data. In addition, the study expands on previous research by using a deep learning framework that classifies breast cancer cells based on segmented collagen properties. This framework uses Long Short-Term Memory (LSTM) and Bidirectional LSTM models. The promise of deep learning for automated cancer detection was demonstrated when the model was trained on segmented collagen pictures to identify the presence of malignant cells. The suggested method provides a notable enhancement in the segmentation of collagen, yielding more dependable outcomes that aid medical professionals in diagnosing breast cancer and developing treatment plans.