The classification of brain magnetic resonance imaging (MRI) images into normal and
abnormal classes, has great potential to reduce the radiologists workload. Statistical …

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 …

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 …

Neurological disorders are abnormalities related to the human nervous system, and
comprise electrical, biochemical, or structural changes in the spinal cord, brain, or central …

Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over
last decades numerous methods had been presented. In this paper, we proposed a novel …

The linear canonical transform (LCT) has been shown to be a powerful tool for optics and
signal processing. Many theories for this transform are already known, but the uniform …

In this paper, we introduce the two‐dimensional short‐time quaternion offset linear
canonical transform (ST‐QOLCT), which is a generalization of the classical short‐time offset …

The free metaplectic transformation (FMT) is widely used in several fields, including filter
design, pattern recognition, image processing, and optics. This study investigated two new …

Finding an appropriate and accurate technology for early detection of disease is significantly
important to research early treatments. We proposed some novel automatic classification …

Wavelet transform is widely used in feature extraction of magnetic resonance imaging.
However, the traditional discrete wavelet transform (DWT) suffers from translation variant …