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首页> 外文会议>2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference >A cancer detection device utilizing multi-tiered neural networks for improved classification
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A cancer detection device utilizing multi-tiered neural networks for improved classification

机译：一种利用多层神经网络进行分类的癌症检测设备

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We introduce a multi-tiered neural network architecture that accurately classifies malignant breast tissue from benign breast tissue. The methodology implemented six different backpropagation neural network (BNN) architectures on 180 malignant and 180 benign breast tissue impedance data files sampled at 47 frequencies from 1 hertz (Hz) to 32 megahertz (MHz). The data were collected utilizing a NovaScan cancer detection prototype device in an approved IRB study at Aurora Medical Center, Milwaukee. The BNN analysis consists of a multi-tiered consensus approach autonomously selecting 4 of 6 neural networks to determine a malignant or benign classification. The BNN analysis was then compared to the histology results with consistent sensitivity of 100 percent and a specificity of 100 percent. This implementation successfully relied solely on statistical variation between the histologically confirmed benign and malignant impedance data and intricate neural network analysis. This approach could be a valuable tool to augment current medical practice assessment of the health of breast and other tissue.
机译：我们引入了多层神经网络体系结构，可以从良性乳腺组织中准确分类出恶性乳腺组织。该方法在以1赫兹（Hz）至32兆赫兹（MHz）的47个频率采样的180个恶性和180个良性乳腺组织阻抗数据文件上实施了六种不同的反向传播神经网络（BNN）结构。数据是在密尔沃基州奥罗拉医学中心的一项批准的IRB研究中使用NovaScan癌症检测原型设备收集的。 BNN分析由多层共识方法组成，该方法可以自主选择6个神经网络中的4个来确定恶性或良性分类。然后将BNN分析与组织学结果进行比较，一致的敏感性为100％，特异性为100％。这种实施仅依靠组织学确认的良恶性阻抗数据和复杂的神经网络分析之间的统计差异来成功实现。这种方法可能是有价值的工具，可以增强当前对乳房和其他组织健康的医学实践评估。

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