Enhanced Deep Learning Frame Model for an Accurate Segmentation of Cancer Affected Part in Breast

Abstract

Breast cancer is one of the primary causes of death occurring in females around the world. So, the recognition and categorization of breast cancer in the initial stage is necessary for helping the patients to have suitable action. In this research, a novel Spider Monkey-based Convolution Model (SMCM) is developed for detecting breast cancer cells in an early stage. Here, breast Magnetic Resonance Imaging (MRI) is utilized as the dataset trained to the system. Moreover, the developed SMCM function is processed on the breast MRI dataset to primarily detect and segment the affected part of breast cancer. Additionally, segmented images are utilized for tracking in the dataset that has identified the possibility of breast cancer. Moreover, the simulation of this approach is done by Python tool and the parameters of the current research work are evaluated with prevailing works. Hence, the outcomes show that the current research model produces improved accuracy for breast cancer segmentation than other models.