Comparative evaluation for detection of brain tumor using machine learning algorithms
Shahab Wahhab Kareem, Bikhtiyar Friyad Abdulrahman, Roojwan Sc. Hawezi, Farah Sami Khoshaba, Shavan Askar, Karwan Muheden, Ibrahim Shamal Abdulkhaleq
Abstract
<span lang="EN-US">Automated flaw identification has become more important in medical imaging. For patient preparation, unaided prediction of tumor (brain) detection in the magnetic resonance imaging process (MRI) is critical. Traditional ways of recognizing z are intended to make radiologists' jobs easier. The size and variety of molecular structures in brain tumors is one of the issues with MRI brain tumor diagnosis. Deep learning (DL) techniques (artificial neural network (ANN), naive Bayes (NB), multi-layer perceptron (MLP)) are used in this article to detect brain cancers in MRI data. The preprocessing techniques are used to eliminate textural features from the brain MRI images. These characteristics are then utilized to train a machine-learning system.</span>