TY - GEN
T1 - Application of artificial intelligence in microwave radiometry (MWR)
AU - Galazis, Christoforos
AU - Vesnin, Sergey
AU - Goryanin, Igor
PY - 2019/2/24
Y1 - 2019/2/24
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Breast Cancer
KW - Cascade Correlation Neural Network
KW - Convolutional Neural Network
KW - Diagnostic System
KW - Machine Learning
KW - Microwave Radiometry
KW - Neural Network
KW - Random Forest
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85064696299&partnerID=8YFLogxK
U2 - 10.5220/0007567901120122
DO - 10.5220/0007567901120122
M3 - Conference contribution
AN - SCOPUS:85064696299
VL - 4
SP - 112
EP - 122
BT - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOINFORMATICS
A2 - De Maria, Elisabetta
A2 - Gamboa, Hugo
A2 - Fred, Ana
PB - SCITEPRESS
T2 - 10th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2019 - Part of 12th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2019
Y2 - 22 February 2019 through 24 February 2019
ER -