Comparative evaluation for detection of brain tumor using machine learning algorithms

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>

Exploring Deep Relationship Learning for Regression: A Case Study on Brain Age Estimation

“…Put another way, why is it important to improve our ways of quantifying ageing from a scientific standpoint? We investigate the possibility that new genetic variables linked to brain ageing can be discovered with the use of a more precise PBA, and that this might improve our ability to characterize brain ageing [8][9]. Potential therapeutic uses of identifying genetic variables related with brain ageing include early detection of brain ageing and administration of appropriate treatments [10,11].…”