Breast Cancer Diagnosis By IoB Implanted Tag Design And Machine Learning

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 non-invasive 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 .

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

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The most deadly and pervasive disease that causes a significant number of fatalities each year is cancer. Lung cancer is the most common and has the greatest fatality rate of all the major types of cancer [1,2]. The most frequent and deadly form of cancer that affects both men and women is lung cancer.

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