| Popis: |
Worldwide, breast cancer is the most common cause of death for women, and increasing survival rates require early identification. Medical scans like mammograms and ultrasounds can be used to predict breast cancer using convolutional neural networks (CNNs). The architecture of CNNs, training data and methods, and performance evaluation criteria are all reviewed in this work as well as the current state of research on CNNs for breast cancer prediction. Also, the benefits and drawbacks of CNNs against conventional techniques for the identification of breast cancer are explored. Although though CNNs have the potential to lower false-positive outcomes and have higher accuracy rates, further testing and study are required to assure their dependability. Also, it’s critical to consider moral concerns like data privacy and bias in machine learning algorithms. Thus, using CNNs to predict breast cancer has enormous potential to increase early detection and, eventually, save lives.So, our objective is to develop a model that can predict breast cancer from mammography images, which will help patients choose between various tests as well as assist medical students in validating their study. |
