A Review of Breast Cancer Detection Using the Internet of Things and Machine Learning

Abstract

Using the Internet of Things (IoT) and Machine Learning (ML), it is possible to find breast cancer quickly and accurately, which is important for getting better and getting treatment right away. In recent years, the IoT has changed in a way that makes it possible to use artificial intelligence (AI) and ML to look at both real-time and historical data. Many imaging methods, such as mammography, thermography, CT, ultrasonography, histology, etc., could find breast cancers in their earliest stages. Medical imaging studies are now heavily reliant on machine learning (ML). With the advancement of machine learning, computer-aided diagnostic (CAD) systems are becoming increasingly smarter and more capable of operating independently. The Internet of Medical Things (IoMT) is a novel method of connecting systems and apps to healthcare services and data sources for monitoring and tracking patients. It can completely change how hospitals work to find and predict cancers with higher care and customer satisfaction. The analysis found that the SVM method had the highest accuracy (98.1%) compared to the other methods. The purpose of this study is to explore the application of imaging and machine learning in conjunction with internet-of-things systems in the detection of breast cancer at an early stage.