Efficient Cancer Detection Using Multiple Neural Networks

Abstract

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.

Mitotic Cell Classification System Based On Supervised Learning for Histopathological Images of Breast Cancer

“…The training set declares the cell as mitotic or nonmitotic by considering the ground truth image. The performance of the work is compared with the existing approaches such as deep belief networks [13], neural networks [21] and Dictionary Learning [22] in terms of accuracy, sensitivity, specificity and time consumption. The formulae for computing the performance metrics are as follows.…”

“…A cancer detection scheme with multiple neural networks is presented in [21]. This work presents a portable desktop prototype device for presenting an accurate neural network classification of malignant and benign tissues.…”

Fundamentals, Recent Advances, and Future Challenges in Bioimpedance Devices for Healthcare Applications

“…Its name refers to its phase angle CPE , which is constant at all frequencies and depends only on the value ( CPE = /2). Typical values of are in the range of 0 ≤ ≤ 1 [68].…”

“…The single-dispersion Cole model and its parameters have been employed in various medical applications, since each parameter of the model has its own physical meaning [12,69]: body composition analysis [70], measurement of the concentration of urea in the dialysate [71], tissue characterization [72][73][74] or analysis of blood samples [75,76], ischemia monitoring [72,77], hydration status evaluation [78], or cancer detection [68,79]. This model has also shown its utility in biology applications, fundamentally in plant physiology [80,81] or the early detection of bacteria [69].…”

“…This is the basis of the cylindrical chamber employed in [97] for the analysis of breast cancer cells, with circular electrodes at the ends as current electrodes and needle electrodes inside as voltage sensing electrodes [97]. In [68], the tetrapolar configuration was used on the same plane to study samples of breast tissue.…”