MRI Image Segmentation: Brain Tumor Detection and Classification Using Machine Learning

Health care is being transformed by machine learning (ML) solutions and artificial intelligence (AI). Healthcare providers must improve patients’ conditions, cut cost expenses, increase efficiency, and make use of the most recent advances in pharmacological therapy if they are to be more effective. In order to modernize the healthcare system, it is necessary to unlock the billions of countless datasets that are typically hidden away in faulty digital systems. The jobs of staff members and physicians can be automated or enhanced with the use of artificial intelligence (AI), which can bring value. Artificial intelligence will entirely automate many monotonous processes, enhancing the efficiency of health practitioners, and the quality of patient outcomes. The use of AI in health care can speed up and reduce the cost of patient care by process automation and analysis of massive patient datasets. AI will have a huge impact on bringing forth more efficiency and innovations in healthcare ecosystem modernization than we can now imagine. This study’s primary goal is to examine the past research on tumor detection along with the future scope that can be taken up by the researchers. Therefore, this research is based on convolution neural networks (CNN), having the capability to examine large amounts of complex images and perform classifications. The proposed method for classifying brain images using CNN delivers an excellent classification efficiency when compared to existing classifiers. CNN has been used as it acts as a strong tool for disease detection and helps doctors in perfect treatment plans with an accuracy of 96.15% and therefore increases the chances of disease prevention.