Artificial Intelligence Assisted Improved Design to Predict Brain Tumor on Earlier Stages using Deep Learning Principle

Brain- Tumors are the second most common reason of cancer today. Numerous people are at risk from cancer. The medical community requires a rapid, automated, efficient, and trustworthy method for detecting tumors, such as brain tumors. Early diagnosis is crucial for effective treatment. If a tumor can be detected early enough, medical professionals can remove the patient from harm's way. This research makes use of novel image processing and deep learning method to predict the brain tumor in an intelligent manner with the power of AI, which is called as Supportive Intelligence for Tumor Detection (SITD). Numerous tumor patients have been saved thanks to this app's accurate diagnosis and therapy. Unchecked cell growth constitutes a tumor's defining feature. As brain tumor cells multiply, they starve healthy brain cells and tissues of the nutrition they need to survive. When trying to locate a tumor in a patient's brain, neurosurgeons must now review MR pictures manually. The goal of this research was to compile a comprehensive literature evaluation on the use of MRI to detect brain cancers for future investigation. This research looked at how advanced learning, transferable learning, and quantum computer learning may be used to analyze brain tumors. Topics included brain tumor anatomy, publically available datasets, augmentation approaches, segmentation, feature extraction, classification and more. The resulting section shows the outcome efficiency of the proposed approach SITD by means of following metrics such as: Training-and- Testing Accuracy, Training-and- Testing Loss.