Computer Aided Diagnosis of Breast Nodule Detection and Mammogram Enhancement Using Fuzzy Sets

Abstract

Breast cancer is widely used in many medical fields. The tumor pandemic drastically influences the health and well-being of the global population. In this proposed methodology, Fuzzy K-Nearest Neighbor Equality approach is performed. The extracted image of Breast cancer cannot be directly used for diagnosis. The captured image contains disturbances like noise, blurred image etc. To get a high-quality image from extracted panoramic. This approach is performed. To the extracted images, partitioning is performed to split up the images into samples. It helps for better recognition and classification. It shows the infected region of Breast cancer accurately. Before performing partitioning, the extracted image has to be preprocessed to clear out the disturbances in the image. After partitioning, feature extraction is performed by using GLCM and finally, classification is performed between the trained and test set data to produce a highly accurate image. It is done by using Fuzzy K-Nearest Neighbor Equality algorithm classifier. This processed image helps the dentist for good prediction. The objective of this work to improve the effectiveness and efficiency of the classification process. In this way to reduce the FP (false positive) rate. The proposed methodology accurately separating the benign and malignant region accuracy