Breast cancer detection
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
NHCE
Abstract
Early identification is essential for improving patient outcomes in breast cancer, one of the major worldwide health concerns. In order to develop a thorough and effective breast cancer detection system, this project combines databases, MATLAB, Flask, and machine learning. Users can easily upload medical data or mammography pictures using the Flask framework as a backend. These inputs are processed by a machine learning model that uses methods like support vector machines (SVM) or convolutional neural networks (CNN) to accurately identify cases as benign or malignant. Additionally, by identifying possible anomalies in mammography pictures, MATLAB-based image processing methods improve the diagnostic workflow.
The MATLAB method uses median filtering to reduce noise, grayscale conversion, and histogram equalization to improve contrast. The Canny edge detection approach is used to identify critical boundaries. Morphological procedures such as dilation and hole filling are then used to further enhance the process and highlight regions of interest. These areas provide a visually understandable depiction of abnormalities and are superimposed in red on the original mammogram.