基于深度学习方法的乳腺癌图像分类

Clenitta Joseph M, Bipin P R, Bobby Mathews C
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引用次数: 0

摘要

乳腺癌是一种始于乳腺细胞的癌症。减轻损害的唯一选择是早期发现和适当的治疗。由于无知或缺乏检测技术,人们故意忽视身体上的问题。深度学习在医学科学领域的应用越来越频繁,它擅长于各种任务,包括分割、检测和分类。本文主要研究使用VGG-16和VGG-19对乳腺癌图像进行分类。分析它们的模型、精度和许多其他方面。VGG-16和VGG-19的图像分类准确率分别为91%和92%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breast Cancer Image Classification Using Deep Learning Methods
Breast cancer is a type of cancer that begins in the breast cell. The only options to lessen the damage are early discovery and appropriate treatment. People willfully disregard physical problems in their bodies due to ignorance or a lack of detection technology. Deep learning is being used more frequently in the field of medical science, and it is good at a variety of tasks, including segmentation, detection, and classification. This article focus on the breast cancer images classification using VGG-16 and VGG-19. Analyze their models, precision, and a number of other aspects. The accuracy of image classification in VGG-16 and VGG-19 is 91% and 92%, respectively.
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