Authors:
Christoforos Galazis
1
;
Sergey Vesnin
2
and
Igor Goryanin
1
;
3
Affiliations:
1
University of Edinburgh, Edinburgh, U.K.
;
2
Medical Microwave Radiometry Ltd., U.K.
;
3
Okinawa Institute of Science and Technology, Okinawa, Japan
Keyword(s):
Microwave Radiometry, Breast Cancer, Diagnostic System, Artificial Intelligence, Machine Learning, Neural Network, Cascade Correlation Neural Network, Convolutional Neural Network, Random Forest, Support Vector Machine.
Abstract:
Microwave radiometry is being developed more actively in recent years for medical applications. One such application is for diagnosis or monitoring of cancer. Medical radiometry presents a strong alternative to other methods of diagnosis, especially with recent gains in its accuracy. In addition, it is safe to use, noninvasive and has a relative low cost of use. Temperature readings were taking from the mammary glands for the purpose of detecting cancer and evaluating the effectiveness of radiometry. Building a diagnostic system to automate classification of new samples requires an adequate machine learning model. Such models that were explored were random forest, XGBoost, k-nearest neighbors, support vector machines, variants of cascade correlation neural network, deep neural network and convolution neural network. From all these models evaluated, the best performing on the test set was the deep neural network with a significant difference from the rest.