A comparative evaluation for Detection Brain Tumor in MRI Image using Machine learning algorithms - Kareem, Shahab; askar, shavan; Abdulkhaleq, Ibrahim; Hawezi, Roojwan Sc. (Jurnal Informatika UPGRIS, 2021)
In medical imaging, automated defect identification of defects has taken on a prominent position. Unaided prediction of tumor (brain) recognition in magnetic resonance imaging process (MRI) is vital for patient preparation. With traditional methods of identifying z is designed to reduce the burden on radiologists. One of the problems with MRI brain tumor diagnosis is the size and variation of their molecular structures. This article uses deep learning techniques (Artificial neural network ANN, Naive Bayes NB, Multi-layer Perceptron MLP ) to discover brain tumors in the MRI scans. First, the brain MRI images are run through the preprocessing steps to remove texture features. Next, these features are used to train a machine learning algorithm.