Comparative evaluation for detection of brain tumor using machine learning algorithms

<span lang="EN-US">Automated flaw identification has become more important in medical imaging. For patient preparation, unaided prediction of tumor (brain) detection the magnetic resonance imaging process (MRI) is critical. Traditional ways recognizing z are intended to make radiologists' jobs easier. The size and variety molecular structures brain tumors one issues with MRI diagnosis. Deep learning (DL) techniques (artificial neural network (ANN), naive Bayes (NB), multi-layer perceptron (MLP)) used this article detect cancers data. preprocessing eliminate textural features from images. These characteristics then utilized train a machine-learning system.</span>