Enhancing Breast Cancer Collagen Segmentation with Improved Swim Transformation Algorithm

Abstract

Breast cancer is a major health problem around the world, and accurately separating collagen fibers in breast cancer cells is important for both detection and planning treatment. Because it can make the differences between fibers and the background stand out more, the Swim Transformation (SWT) algorithm has shown promise in separating collagen fibers. However, the original SWT algorithm has some flaws, such as being easily affected by noise and artifacts. We suggest a better version of the SWT algorithm in this study to fix these problems and make collagen segmentation in breast cancer tissues better. A new step in the preparation process was added to cut down on noise, and adaptable thresholding was added to make it easier to find collagen fibers. We also add a step called "post-processing" to get rid of any errors and make the segmentation results better. We used a collection of microscopic images of breast cancer tissues in tests to see how well the proposed method worked. For segmentation, we looked at how our better SWT algorithm did compared to the original SWT algorithm and other cutting-edge segmentation methods. Based on our data, the suggested method does a better job in terms of accuracy, sensitivity, and precision. In addition, we add a deep learning model for finding breast cancer that uses Long Short-Term Memory (LSTM) and Bidirectional LSTM models to our work. A set of images of breast cancer is used to teach a deep learning model that can tell from collagen segmentation results whether or not there are dangerous cells. The paper shown an improved SWT algorithm for separating collagen in breast cancer cells. This algorithm fixes the problems with the first one and gets better results for separating collagen. It's possible that the suggested formula will help doctors find and plan better treatments for breast cancer by making it easier to separate collagen fibers more accurately.