乳腺癌cad系统应用迁移学习,提高ROI

Q3 Economics, Econometrics and Finance
Muayed S. Al-Huseiny, Ahmed S. Sajit
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引用次数: 0

摘要

计算机系统正被用于医疗诊断等专业领域,以减轻一些成本并提高可靠性和可扩展性。本文实现了一个计算机辅助癌症诊断系统。它利用了公开可用的迷你MIAS乳房X光摄影图像数据集。对图像进行预处理以清洁分离乳房组织区域。提取的区域用于调整和验证预训练的卷积深度神经网络GoogLeNet。与其他已发表的工作相比,所实现的模型显示出良好的性能结果,准确率为86.6%,灵敏度为75%,特异性为88.9%。
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 CAD SYSTEM BY USING TRANSFER LEARNING AND ENHANCED ROI
Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%. 
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来源期刊
Applied Computer Science
Applied Computer Science Engineering-Industrial and Manufacturing Engineering
CiteScore
1.50
自引率
0.00%
发文量
0
审稿时长
8 weeks
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