Affiliations: [a] Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt | [b] Biomedical Engineering Department, Medical Research Institute, Alexandria University, Alexandria, Egypt | [c] Histochemistry and Cell Biology Department, Medical Research Institute, Alexandria University, Alexandria, Egypt | [d] Pathology Department, Medical Research Institute, Alexandria University, Alexandria, Egypt
Correspondence:
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Corresponding author: Mamdouh M. Shawki, Medical Biophysics Department, Medical Research Institute, Alexandria University, 165 Alhorya Avenue, Alexandria, Egypt. Tel.: +20 1123034145; E-mail: mamdouh971@hotmail.com.
Abstract: BACKGROUND: The early detection of human breast cancer represents a great chance of survival. Malignant tissues have more water content and higher electrolytes concentration while they have lower fat content than the normal. These cancer biochemical characters provide malignant tissue with high electric permittivity (ε´) and conductivity (σ). OBJECTIVE: To examine if the dielectric behavior of normal and malignant tissues at low frequencies (α dispersion) will lead to the threshold (separating) line between them and find the threshold values of capacitance and resistance. These data are used as input for deep learning neural networks, and the outcomes are normal or malignant. METHODS:ε´ and σ in the range of 50 Hz to 100 KHz for 15 human malignant tissues and their corresponding normal ones have been measured. The separating line equation between the two classes is found by mathematical calculations and verified via support vector machine (SVM). Normal range and the threshold value of both normal capacitance and resistance are calculated. RESULTS: Deep learning analysis has an accuracy of 91.7%, 85.7% sensitivity, and 100% specificity for instant and automatic prediction of the type of breast tissue, either normal or malignant. CONCLUSIONS: These data can be used in both cancer diagnosis and prognosis follow-up.
Keywords: Breast cancer, dielectric properties, deep learning neural network, α dispersion
