乳腺癌检测的人工智能模型比较研究

Tanvi Meet Dhruv
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引用次数: 0

摘要

妇女中最常见的癌症是乳腺癌。根据世界卫生组织(WHO)的统计数据,到 2020 年,乳腺癌将导致全球约 23 亿妇女死亡,死亡人数达 6.859 亿。机器学习和深度学习技术被认为是有用的方法,因此在乳腺癌检测方面引起了研究人员的关注。此外,通过提取手工制作的特征,它还能极大地帮助乳腺癌的预先检测和预测过程。然而,近年来,人工智能(AI)的进步使得深度学习策略(如 CNN)和迁移学习法在乳腺癌检测中得到了成功应用。深度学习方法使用了大量的数据集。它不需要人工干预特征提取,因此提高了患者的生存几率。本综述论文基于使用深度学习和基于机器学习的癌症检测技术进行乳腺癌检测,以帮助了解癌症检测的趋势和挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Study of Artificial Intelligence Models for Breast Cancer Detection
The most prevalent type of cancer among women is breast cancer. According to the statistics given by the World Health Organization (WHO), breast cancer is the reason behind the death of about 2.3 billion women globally in 2020, accounting for 685.9 million deaths. Since they are thought to be useful approaches, machine learning and deep learning techniques have drawn attention from researchers in breast cancer detection. Also, it can significantly assist in the process of prior detection and prediction of breast cancer by extracting handcrafted features. However, in recent years, improvements in artificial intelligence (AI) have enabled the successful use of deep learning strategies like CNN and the transfer learning method for detection of breast cancer. A significantly large dataset is used for deep learning methods. It does not require human intervention for feature extraction, which, as a result, enhances the patient's chances of survival. This review paper is based on breast cancer detection using deep learning and machine learning-based cancer detection techniques to aid in the understanding of trends and challenges in cancer detection.
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