Segmentation of tumors in human brain aims to classify different abnormal tissues (necrotic
core, edema, active cells) from normal tissues (cerebrospinal fluid, gray matter, white matter) …

The uncontrolled growth of cells in brain regions leads to the tumor regions and these
abnormal tumor regions are scanned by magnetic resonance imaging (MRI) technique as …

To design an efficient partial differential equation-based total variation method for denoising
and possibilistic fuzzy c-means clustering algorithm for segmentation and these methods …

Background Microarray datasets consist of complex and high-dimensional samples and
genes, and generally the number of samples is much smaller than the number of genes …

Medical image processing is typically performed to diagnose a patient's brain tumor prior to
surgery. In this study, a technique in denoising and segmentation was developed to improve …

The detection and segmentation of tumor region in brain image is a critical task due to the
similarity between abnormal and normal region. In this article, a computer‐aided automatic …

The development of abnormal cells in human brain leads to the formation of tumors. This
article proposes an efficient approach for brain tumor detection and segmentation using …

This paper presents the development of an automatic system for the classification of tooth
wear disease diagnosis. Abnormal detection, disease detection and classification of oral …

Background Diagnosing brain tumours remains a challenging task in clinical practice.
Despite their questionable accuracy, magnetic resonance image (MRI) scans are presently …

The meningioma tumors are classified and segmented using soft computing methods in this
paper. The noise contents are detected and reduced using directional filters and then Gabor …