An automatic brain tumor segmentation method based on texture feature and kernel sparse
coding from FLAIR (fluid attenuated inversion recovery) contrast-enhanced MRIs (magnetic …

Abstract Background and Objectives Melanoma is a dangerous kind of skin disease with a
high death rate, and its prevalence has increased rapidly in recent years. Diagnosis of …

Building highly nonlinear and nonparametric models is central to several state-of-the-art
machine learning systems. Kernel methods form an important class of techniques that …

The automation in the diagnosis of medical images is currently a challenging task. The use
of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially …

Brain tumor segmentation, an essential but challenging task, has long attracted much
attention from the medical imaging community. Recently, successful applications of sparse …

A robust method to automatically segment and identify tumor regions in medical images is
extremely valuable for clinical diagnosis and disease modeling. In various embodiments, an …

Advances in the field over the years have made medical imaging an indispensable part of
medicine. Today, the use of medical images is often critical for diagnosis and treatment …

Kernel fusion is a popular and effective approach for combining multiple features that
characterize different aspects of data. Traditional approaches for Multiple Kernel Learning …

Brain tumor classification is considered as one of the most challenging tasks in medical
imaging. In this paper, a novel approach for multi-class brain tumor classification based on …

The segmentation of brain tumor plays an important role in diagnosis, treatment planning,
and surgical simulation. The precise segmentation of brain tumor can help clinicians obtain …