<![CDATA[Breast cancer is the second most common cause of mortality for women, after lung cancer. Women's death rates can be decreased if breast cancer is identified early. The artificial intelligence model has the ability to predict breast cancer with the same level of accuracy as an experienced radiology technician. For early cancer detection, an automated approach is necessary because manual breast cancer diagnosis is time-consuming. Deep learning is a type of artificial intelligence that enables software applications to predict more accurate results without being explicitly programmed. The main objective of this paper is to evaluate the performance of a general deep learning algorithm (DLS) with human readers with varying degrees of breast imaging experience in order to train it to identify cancer of the breast on ultrasound pictures. Moreover, this study will examine five deep learning methods that have aided in breast cancer prediction, these are Convolutional Neural Network (CNN), Genetic Algorithm GA-CNN, Deep Belief Network (DBN), Computer Aided Diagnosis (CAD), and Generative Adversarial Networks (GAN). Our main goal is to identify the most appropriate and accurate algorithm for the prediction of breast cancer.]]>
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