Published Paper Details:

Breast cancer is one of the leading causes of death in women. Many studies on the diagnosis and detection of breast cancer have been conducted using various image processing and classification techniques. Despite this, the disease remains one of the deadliest. Having given birth to one out of every six women in her lifetime. Early detection of breast cancer tumours is the only way to cure it. The most efficient and simple way to diagnose breast cancer is to use CAD (Computer Aided Diagnosis) on mammographic images. Accurate discovery can significantly reduce the mortality rate caused by mamma cancer. Clusters of masses and microcalcifications are important early symptoms of breast cancer. They can aid in the early detection of breast cancer. This paper quantifies the image analysis methods used for tumour detection. These tumours are extracted from the mammogram ROI using FCM to classify the microcalcifications as harmless, ordinary, or dangerous. These images are compared further and run through CNN to gain a better understanding of the cancer pattern in the mammography image.