Developing a Model to Identify and Analyze Features in Mammography Images to Detect Breast Cancers
Hayder Raheem Salman AL-Hraishawi, Ali H. Hamie · International Research Journal of Innovations in Engineering and Technology · 2024
Worldwide, breast cancer is the primary cause of mortality associated with cancer in females.Swift detection, classification, and assessment of this neoplasm can greatly diminish the corresponding fatality rate.Physical examinations have been supplanted by digital mammography as the prevailing technique for identifying breast cancer.Machine learning can use medical files and imagery to improve the early identification of conditions, optimize remedy consequences.The accuracy of determining whether or not the person with most cancers or no cancer based totally at the kind of approach which utilized for prognosis.Therefore, this study at built a convolutional neural network to extract characteristics from the DDSM mammography dataset, which have been trained and tested with several machine learning algorithms.The system achieved a detection accuracy of as much as 94% for breast cancer the usage of numerous categorization algorithms.This outcome holds good sized significance and practicality in enhancing the control of this disease and advancing its identification.