In a short period of time, many areas of science have made a sharp transition towards data-
dependent methods. In some cases, this process has been enabled by simultaneous …

Brain disease such as brain tumor, Alzheimer's disease, etc. is a major public health
problem, and the main cause of death worldwide. Expert systems are gaining much attention …

Despite the success of automated pattern recognition methods in problems of human brain
tumor diagnostic classification, limited attention has been paid to the issue of automated …

Purpose To investigate and compare human judgment and machine learning tools for
quality assessment of clinical MR spectra of brain tumors. Methods A very large set of 2574 …

The dominant paradigm for modeling the obesity-induced T2DM (type 2 diabetes mellitus)
today focuses on glucose and insulin regulatory systems, diabetes pathways, and diagnostic …

Restoring information obstructed by hair is one of the main issues for the accurate analysis
and segmentation of skin images. For retrieving pixels obstructed by hair, the proposed …

Glioblastoma is the most frequent malignant intra-cranial tumour. Magnetic resonance
imaging is the modality of choice in diagnosis, aggressiveness assessment, and follow-up …

The power of artificial neural networks to form predictive models for phenomenon that exhibit
non-linear relationships is a given fact. Despite this advantage, artificial neural networks are …

One of the most important contributions of topic modeling is to accurately and the ectively
discover and classify documents in a collection of texts by a number of clusters/topics …

Artificial neural networks (ANNs) are ideal for the prediction and classification of non-linear
relationships however they are also known for computational intensity and long training …