MULTI-MODAL IMAGING AND AI FOR AUTOMATED BREAST CANCER DIAGNOSIS SUPPORT SYSTEM (International Journal of Food and Nutritional Sciences, 2021)
Breast cancer is a prevalent disease worldwide, with over 1.15 million cases diagnosed annually. Currently, the clinical management of breast cancer relies on a limited number of accurate prognostic and predictive factors. Early detection plays a crucial role in reducing mortality rates and improving the survival period of breast cancer patients. Mammography is the primary screening and diagnostic test used, and the analysis and processing of mammograms are key to enhancing breast cancer prognosi.In this project, the detection of breast cancer in mammograms is achieved through image segmentation using the Fuzzy C-means (FCM) technique. The FCM algorithm is applied to segment the mammogram into distinct regions. Following segmentation, features are extracted from these segmented regions. The extracted features are then utilized to train a classifier capable of accurately categorizing different classes in mammograms. Texture features, which provide important information about the texture patterns within the mammogram, are extracted using techniques such as multi-level Discrete Wavelet Transform, Principal Component Analysis (PCA), and Gray-level Cooccurrence Matrix (GLCM).To distinguish masses and microcalcifications from the background tissue, morphological operators are employed. These operators help in identifying and separating tumor-affected regions from the surrounding healthy tissue. The K-nearest neighbors (KNN) algorithm is utilized as a classification technique for assigning mammogram images to their respective classes based on the extracted features.The boundaries of the tumor-affected regions in the mammogram are marked and displayed to the doctor for further examination. Additionally, the area of the tumor is provided to assist in evaluating the size and extent of the tumor. This information aids in the diagnosis and treatment planning for breast cancer patients. It’s important to note that while this description provides an overview of the project, the implementation details and specific algorithms used may vary. It is crucial to conduct further research and refer to academic papers or specific sources to gain a comprehensive understanding of the techniques and methods employed in breast cancer detection using mammography.