AUTOMATIC INTERPRETATION MODEL OF RADIOGRAPHIC BRAIN IMAGE AND ITS APLICATION IN THE PYTHON METHODS A SURVEY

Abstract

Investigation and analysis of diseases caused by brain abnormalities and disabilities using the image of the brain is very helpful to the doctor in the quality of reading medical images so that a clinical diagnostic decision model is generated to determine the diagnosis and appropriate treatment steps for all patients with effective and accurate. The application machine learning of python programming language technology is used to design and develop software application models to analyze and explore knowledge of the patient's brain
abnormalities image and facilitate the use of many advanced learning machines, pattern recognition and multivariate statistical techniques on brain imaging data for applications such as mutli-voxel pattern analysis, decoding, predictive modeling, functional connectivity, brain parcellations and connectomes. Clinical diagnostic decision making in brain imaging has been improved by functional magnetic resonance imaging (MRI) systems, serving as second readers to detect suspicious nodules for diagnosis by a radiologist. Brain Image analysis and
interpretation is generally a process where digital brain image processing is utilized to process digital brain images in order to extract significant statistics or information from the medical images. The machine learning interpretation process enables to analyze and visualize medical images with python and machine learning neuroimaging technology of numerous modalities. A brain image interpretation is basically analyzed from the perspective of its machine learning, segmentation, edge detection, registration and morphology or motion
analysis. Each method is discussed and analyzed through its applications, advantages, limitations and results.