Neural network based breast cancer classifier using electrical impedance

Abstract

Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, neural network based classifier is proposed which used the electrical impedance data as input data set. Multi-Layer Perceptron (MLP), network is designed with systematic experimentation. In our experiments, we utilize the criteria of mean squared error and absolute classification accuracy to concretely evaluate the performances of the NN based classifier. The experimental results demonstrate that the proposed classifier works as an elegant classifier with the accuracy of 96% Index Terms-Breast cancer, Electrical impedance, MLP

A survey of breast cancer screening techniques: thermography and electrical impedance tomography

“…Acc: 92% [80] Modeling of EIT distribution in a breast with a tumor, using ANN, PCA amd BEM ANN Abs. E: 0.23 [84] Review of the main advances in EIT for breast cancer diagnosis No MLT - [81] First test ever of EIT in breast cancerous tissue (1926) No MLT - [82] REIS system with 7 electrodes, 1 in the center, and 6 concentrically separated.REIS showed high false positive rate ANN Acc: 67% Sen: 54% Sp: 90% [85] Multi-Layer perceptron algorithm for the EIT data set from [22,80] ANN -MLP Acc: 96% MSE: 0.1 CC: 0.99 [86] CAD system for breast cancer classification in the EIT data set from [22,80] LR + NBN Acc-1: 97.5% Acc-2: 89.7% Acc-3: 77.35% [90] EIT system for early detection of breast cancer in 1103 women Multi LR - [87] 10 women clinical study, using 2 different setups with a EIT-Probe LDA, LSE - [88] OCCII-GIC system for make a map of the breast, using 85 electrodes and frequencies from 10kHz to 3MHz -- [89] The background of the electrical impedance tomography as an early breast cancer diagnosis system, is consider above, nonetheless the EIT devices were not studied, with this intention, the next section, will present the main EIT devices, mostly are not on the market yet.…”

“…A part of the researchers are focusing in developing 3D models with diverse electrical tissue properties [84], another part in portable and non-portable devices [19,[91][92][93]95,96] as shown in Table 6. Furthermore, the phenomenon and performance with CAD systems are reviewed [77,[80][81][82][83][85][86][87][88][89][90]97], and finally, two teams suggest a considerable reduction in FP and FN rates in electro-thermal CAD systems [23,98]. To put it differently, the miniaturization of electronic devices and the hyper-connection of a globalized world suggest that in the short-term future, many new wearable and portable devices will track our health day-to-day.…”

“…The team makes an equidistance arrangement of a seven electrode-probe on each patient, and then the data goes to a CAD system for interpretation purposes. In addition to ANN for EIT breast cancer prediction, Shetive, et al in 2012 create a multi-layer perceptron 9 (MLP) classifier model who achieved a 96% accuracy [86] on the Jossinet et al database [22,80].…”

A Survey of Breast Cancer Screening Techniques: Thermography and Electrical Impedance Tomography

“…The team makes an equidistance arrangement of a seven electrode-probe on each patient, and then the data goes to a CAD system for interpretation purposes. In addition to ANN for EIT breast cancer prediction, Shetive, et al [86] in 2012 create a multi-layer perceptron 9 (MLP) classifier model who achieved a 96% accuracy on the Jossinet et al database [22,80]. Similarly, logistic regression, KNN and Naive Bayesian networks were used by Calle-Alonso et al [87] to classify the EIT data set from [22,80].…”

“…In essence, the table 4: "Acc-1" refers to two-classes, "Acc-2" to three-classes and "Acc-3" to six-classes approach. Multi-Layer perceptron algorithm for the EIT data set from [22,80] ANN -MLP Acc: 96% MSE: 0.1 CC: 0.99 [86] CAD system for breast cancer classification in the EIT data set from [22,80] LR + NBN Acc-1: 97.5% Acc-2: 89.7% Acc-3: 77.35% [87] EIT system for early detection of breast cancer in 1103 women Multi LR - [88] 10 women clinical study, using 2 different setups with a EIT-Probe LDA, LSE -…”