Enhancing Breast Cancer Diagnosis Through Virtual Biopsy and Machine Learning

Abstract

Breast cancer continues to be a leading global health concern, and early detection is the key to optimizing prognosis and survival. Traditional diagnostic approaches have consisted of mammograms, ultrasound, and invasive biopsies — which have been effective in detecting tumors but has its drawbacks. Mammography frequently misses tumors in women with dense breast tissue, and biopsies are costly, invasive and time-consuming. And they also inflict pain and suffering on the patients. There is a need for less invasive and more effective diagnostic alternatives to such facts. This latest technique gives hope as non-invasive diagnostic method including preparation of virtual biopsy. Virtual biopsy refers to using advanced imaging technologies such as MRI, ultrasound, and mammography to see the properties of the breast tissue without removing it. This would enable the improved and more efficient identification of tumors, enabling less invasive biopsies to be performed, and subsequently less discomfort for patients. The main objective of this paper is to represent virtual biopsy with machine learning algorithms to enhance early breast cancer detection which includes SVM, Decision Tree and CNN. Machine learning algorithms proved to be very successful in medical image analysis. SVM can solve complex problems with high accuracy by deciding his best decision boundaries, Decision Tree provides overfitting, and CNN automatically finds features from images, which is very suitable for detecting subtle features in medical images. The results of this study show that the combination of virtual biopsy with machine learning significantly improves diagnostic accuracy and efficiency. This will allow the detection to happen much sooner, with fewer repeat tests, and will be much more reliable, non-invasive, than traditional diagnostic methods. An accurate alternative to invasive techniques, virtual biopsy, combined with machine learning promises to enhance the early detection of breast cancer, allowing for better patient outcomes while easing the burden on the healthcare system.