ABSTRACT
Brain tumor is an abnormal tissue growth inside the human skull. In the health care sector, many doctors and researchers are examining for the early prediction of brain tumor disease which leads to many risk factors like brain cancer, brain diseases that can be identified in brain tumor surveillance decision support systems. One of the most popular ways for detection of brain tumor is by analyzing the important information about abnormal tissues that are present in Magnetic Resonance Images (MRIs). As there are lots of improvement and research growth in clinical diagnosis to perform, monitor, and analyze many complex tasks in various fields of medical imaging using machine learning algorithms and medical robotic imaging using deep learning algorithms. However, automatic detection of brain tumor using machine learning algorithms and its early detection of risk factors using nano-robotic health care systems is a challenging and novel approach. Automatic detection of brain tumor and its risks using nano-robotics will focus more on present art of the technology. This chapter highlights the importance of machine learning algorithms toward nano-robotic systems that will provide high statistical measures like accuracy, sensitivity, precision, etc., for the real-time monitoring with nano-robotics by using mobile phones, satellites, sensors, 88etc. It benefits in minimizing the potential risks connected to human health or environment toward nanotechnologies. This novel approach is highly efficient and effective for future applications by enhancing online monitoring automatic detection of brain tumor nano-robotic systems.
