A novel algorithm for detection and segmentation of brain tumor in mri images

Abstract

newline The advent of technology has deeply ventured into the medical industry for monitoring, preventing and managing the various ailments of patients. With the introduction of machine learning, deep learning and artificial intelligence based algorithms and models in the medical domain, the practice has become more sophisticated with numerous advancements. There are thousands of medical conditions which have been automated with computer aided diagnosis applications and the same is inevitable for life saving measures. Tumour detection and classification is a primary focus of the medical industry, claiming to be the major reasons for mortality across all age groups. The amount of manual work involved in obtaining, capturing, classifying and then planning for a proper diagnosis is time consuming and tedious, giving room for errors. Given the criteria of early detection and timely treatment to patients with different classes of tumour, the domain has welcomed advanced and automated solutions with the power of machine learning and artificial intelligence algorithms. Radiologists and other associated medical experts are prone to tedious tasks with detection and classification of brain tumours despite the availability of simplified modern medical imaging technologies. Detection and classification are subjected to maximum accuracy, with a keen eye on varying progress or regress based on medications. Depending on the years of experience and qualifications, the accuracy of manual detection methods increases and hence the demand for computer aided diagnosis approaches have opened up new areas of research and development