Abstract
This article provided a comprehensive overview of the current state of breast imaging machine learning methods. Breast cancer is the second leading cause of cancer deaths among women, behind lung cancer. To make an accurate diagnosis of breast cancer, pathologists use a systematic and objective process that begins with the categorization of discovered tumors. The application of machine learning techniques has dramatically improved breast cancer staging and diagnosis. One million women are diagnosed with breast cancer each year. An effective detection system should produce few false positives. In the past, we would get where we needed to go by reviewing the most current research that attempted to classify these cancers. Machine learning algorithms like support vector machine (SVM), K-NN, and random forest are used to determine if a medical image is cancerous or benign. Preliminary preprocessing and feature selection in traditional machine learning take a considerable amount of time and computer resources, hence recent studies have included deep learning. There has been an increase in the usage of CNNs for the classification of breast cancers. This investigation provides a comprehensive literature review of machine learning-based methods for breast cancer detection, which may be useful to both researchers and clinicians.
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Saraswat, S., Keswani, B., Saraswat, V. (2023). Classification of Breast Cancer Using Machine Learning: An In-Depth Analysis. In: Tripathi, A.K., Anand, D., Nagar, A.K. (eds) Proceedings of World Conference on Artificial Intelligence: Advances and Applications. WWCA 1997. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-5881-8_16
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