To classify brain images into pathological or healthy is a key pre‐clinical state for patients.
Manual classification is tiresome, expensive, time‐consuming, and irreproducible. In this …

Background: Automated and accurate classification of MR brain images is of crucially
importance for medical analysis and interpretation. We proposed a novel automatic …

(Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step
for potential patients. Manual classification is irreproducible and unreliable. In this study, we …

Automated and accurate classification of MR brain images is extremely important for medical
analysis and interpretation. Over the last decade numerous methods have already been …

This paper presents a hybrid technique for the classification of the magnetic resonance
images (MRI). The proposed hybrid technique consists of three stages, namely, feature …

Automated and accurate classification of MR brain images is of importance for the analysis
and interpretation of these images and many methods have been proposed. In this paper …

The present paper proposes the development of a new approach for automated diagnosis,
based on classification of magnetic resonance (MR) human brain images. Wavelet transform …

Aim: To detect pathological brain conditions early is a core procedure for patients so as to
have enough time for treatment. Traditional manual detection is either cumbersome, or …

A technique for magnetic resonance brain image classification using perceptual texture
features, fuzzy weighting and support vector machines is proposed. In contrast to existing …

The unprecedented improvements in computing capabilities and the introduction of
advanced techniques for the analysis, interpretation, processing, and visualization of images …