论文标题
使用人工智能技术检测乳腺癌：系统文献综述
Breast cancer detection using artificial intelligence techniques: A systematic literature review
论文作者
论文摘要
癌症是人类最危险的疾病之一，但尚未开发永久治愈。乳腺癌是最常见的癌症类型之一。根据国家乳腺癌基金会的数据，仅在2020年，在美国诊断出了276,000多例新的侵入性乳腺癌病例和48,000多例非侵入性病例。为了看这些数字，这些病例中有64％是在疾病周期的早期被诊断出来的，使患者的生存机会99％。人工智能和机器学习已被有效地用于检测和治疗几种危险疾病，在早期诊断和治疗方面有助于，从而增加了患者的生存机会。深度学习旨在分析影响严重疾病发现和治疗的最重要特征。例如，可以使用基因或组织病理学成像检测乳腺癌。遗传水平的分析非常昂贵，因此组织病理学成像是用于检测乳腺癌的最常见方法。在这项研究工作中，我们在深度学习和机器学习的帮助下，系统地检查了使用遗传测序或组织病理学成像对乳腺癌进行检测和治疗的先前工作。我们还向将在该领域工作的研究人员提供建议
Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease's cycle, giving patients a 99% chance of survival. Artificial intelligence and machine learning have been used effectively in detection and treatment of several dangerous diseases, helping in early diagnosis and treatment, and thus increasing the patient's chance of survival. Deep learning has been designed to analyze the most important features affecting detection and treatment of serious diseases. For example, breast cancer can be detected using genes or histopathological imaging. Analysis at the genetic level is very expensive, so histopathological imaging is the most common approach used to detect breast cancer. In this research work, we systematically reviewed previous work done on detection and treatment of breast cancer using genetic sequencing or histopathological imaging with the help of deep learning and machine learning. We also provide recommendations to researchers who will work in this field
