A review on data mining techniques for analysis and prediction of tumors in Mammogram images.

Breast cancer is one of the most lethal forms of cancer in women worldwide, and its diagnosis and classification presents a significant challenge to the medical community. Breast cancer is the second leading cause of mortality among adult women, and its prevalence has been rising rapidly over the last several decades. Due to the high mortality rate and difficulty of treatment, late-stage breast cancer diagnosis is a major reason why early detection is so important. Mammography, Ultrasound, and other similar diagnostics are offered for early detection and recovery. It has been determined that mammography is the most effective screening tool for breast cancer. The primary goal of this study is to evaluate the performance of the proposed clustering algorithm against those of the two baseline methods and to use classification algorithms to validate the reliability of the findings. Algorithms for grouping and classifying data are tested, and their results are evaluated depending on how well they locate sections of the body that have been damaged by a tumor. [ABSTRACT FROM AUTHOR]