Radiomics analysis has had remarkable progress along with advances in medical imaging,
most notability in central nervous system malignancies. Radiomics refers to the extraction of …

Brain tumors are a widespread and serious neurological phenomenon that can be life-
threatening. The computing field has allowed for the development of artificial intelligence …

Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance
imaging (MRI) is an essential procedure in the field of medical imaging analysis for full …

Cancer is considered one of the most aggressive and destructive diseases that shortens the
average lives of patients. Misdiagnosed brain tumours lead to false medical intervention …

Haralick texture features are common texture descriptors in image analysis. To compute the
Haralick features, the image gray-levels are reduced, a process called quantization. The …

Radiomics-based researches have shown predictive abilities with machine-learning
approaches. However, it is still unknown whether different radiomics strategies affect the …

Texture analysis has arisen as a tool to explore the amount of data contained in images that
cannot be explored by humans visually. Radiomics is a method that extracts a large number …

Purpose To explore the feasibility and diagnostic performance of radiomics based on
anatomical, diffusion and perfusion MRI in differentiating among glioma subtypes and …

Recent developments in glioma categorization based on biological genotypes and
application of computational machine learning or deep learning based predictive models …

Lung cancer causes more than one million deaths worldwide each year. Averages of 5-year
survival rate of patients with Non-small cell lung cancer (NSCLC), which is the most common …