Detection of breast cancer and fibroids using image processing

Abstract

Breast cancer and uterine fibroids are significant health concerns newlineaffecting millions of women globally. Breast cancer is one of the leading causes newlineof cancer-related mortality globally and is the most frequently diagnosed cancer newlinein women. Accounting for 22% of new cancer cases annually, breast cancer newlinerepresents a significant public health issue with high social and economic newlineprevalence continues to grow. Early detection of breast cancer offers the best newlinechance for a cure and mortality rate can be reduced. Mammography is the newlineleading tool for early breast disease detection, enabling healthcare providers to newlineidentify potential issues before symptoms emerge. However, detecting key newlinediagnostic indicators, such as microcalcifications (small calcium deposits that newlinecan signal early cancer) and abnormal masses, remains challenging. This is newlinelargely due to the low-contrast and grainy quality of mammogram images, newlinewhich can obscure these subtle features. Enhancements in imaging technology newlineand advanced processing methods are being explored to address these newlinelimitations, aiming to improve the clarity and reliability of mammograms for newlinemore accurate diagnosis. Uterine fibroids, or leiomyomas, are among the most newlinefrequently occurring gynecological tumors, representing a significant health newlinechallenge for women, particularly during their reproductive years. With an newlineestimated prevalence rate of 20-50% among women of reproductive age, newline newlineThe accurate detection and evaluation of uterine fibroids often rely on imaging newlinetechniques such as ultrasound, which is a widely used, non-invasive, and newlinerelatively low-cost tool for diagnosis. newline