Abstract
Breast cancer is one of the worst diseases in the world and the most common cancer affected by women. Early
detection of cancers allows for faster treatments. Recent studies have focused on early breast cancer diagnosis utilizing noninvasive UWB technologies. This article proposed to use metamaterials as an Implantable antenna to detect breast cancer in
filed of IOB. With non-toxic materials,and safity frequency range from 1 to 10 GH three different compact and comfortable
sizes for metamaterial antennas have been used for implanted within the breast tissue. Two models for compressed breast tissue
were created using the CST Microwave studio simulator. These models generated patient data set with differing dielectric
properties similar to human tissue. These dataset are used to train several appropriate supervised machine learning algorithms:
Decision tree (DT), support vector machine (SVM), and nearest neighbour (NN) in order to develop an intelligent classification
model that can assist doctors in identifying malignant breast cells. As a result SVM can classify the breast data to detect the
tumor affactivly with 93%accuracy .
detection of cancers allows for faster treatments. Recent studies have focused on early breast cancer diagnosis utilizing noninvasive UWB technologies. This article proposed to use metamaterials as an Implantable antenna to detect breast cancer in
filed of IOB. With non-toxic materials,and safity frequency range from 1 to 10 GH three different compact and comfortable
sizes for metamaterial antennas have been used for implanted within the breast tissue. Two models for compressed breast tissue
were created using the CST Microwave studio simulator. These models generated patient data set with differing dielectric
properties similar to human tissue. These dataset are used to train several appropriate supervised machine learning algorithms:
Decision tree (DT), support vector machine (SVM), and nearest neighbour (NN) in order to develop an intelligent classification
model that can assist doctors in identifying malignant breast cells. As a result SVM can classify the breast data to detect the
tumor affactivly with 93%accuracy .
Keywords
Breast Cancer
implanted antenna
IOB
machine learning
Metamaterial
SAR