Mammogram image modalities utilizing novelty of fuzzy segmentation for image enhancement of breast cancer identification

Breast Cancer is one of the most dangerous and deadly diseases. It is related to early identification of a mammogram X-ray tool diagnostic, the breast cancerous, non-cancerous and normal tissue identification with radiologist findings. It uses a Mammogram Image Analysis of Society (MIAS) database utilizing benchmark dataset to identify breast cancer with enhancing images. The main methodology of image segmentation utilizing the heart of the methods is K-Means (KMs) based on clustering and classification. There are also findings about image enhancement for multi-models such as the performance of KMs, KM++, GMM, FKM, FCM and FRR. These methods are evaluated for image enhancement. Image segmentation of machine learning approaches is one of the methods: K-Means based image segments to various methods enhancing statistical measurements of PSNR, SNR, MSE, IoU, DSC, JI. These metrics are image quality metrics. The classification and prediction-based result findings are precision, accuracy, specificity, sensitivity and F-measures. Finally, performance computing with python uses better results for image quality metrics and image segmentation of breast cancer identification.