A cancer detection device utilizing multi-tiered neural networks for improved classification

Shell, John; Gregory, W. D.

doi:10.1109/hic.2016.7797705

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…The laptop also provides a graphical user interface to monitor the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${Z}^\ast $ \end{document} components and view the final Cole analysis result. A local regression non-parametric smoothing method, Loess [13] – [15] , is applied to the data before the smoothed data is then fit to the Cole-Cole function where the Cole relaxation constant, fc is extracted. This parameter is then used to designate or classify data files cancer (CA) or benign (BE).…”

Section: Methods and Proceduresmentioning

confidence: 99%

Efficient Cancer Detection Using Multiple Neural Networks

Shell

Gregory

2017

IEEE J. Transl. Eng. Health Med.

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The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

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Section: Methods and Proceduresmentioning

confidence: 99%