Detection of Brain Tumors from MRI Images based On Development of Thinking Computer systems Techniques

Abstract

Brain tumors are one of the common diseases of the nervous system and have great harm to human health, and even lead to death. The detection, segmentation, and extraction of contaminated tumour regions from Magnetic Resonance Imaging (MRI) pictures are major problems; yet, a repetitive and time-consuming task performed by radiologists or clinical experts relies on their experience. The many anatomical structures of the human organ can be imagined using image processing concepts. Detection of human brain abnormal structures by basic imaging techniques is challenging AI solutions must be evidence-based, and all AI tools used in radiology are expected to comply with the STARD (Standards for Reporting Diagnostic Accuracy. Whether AI is used as an alternative, substitute, or adjunct to radiologic workflows, the application of machine learning and deep learning, i.e., based on data acquisition and monitoring without prior programming, has been shown to facilitate the detection, segmentation, and classification of images and lesions. Patients benefit from automated and rapid detection of critical findings with appropriate imaging quality.