The detection of a brain tumor and its classification from modern imaging modalities is a
primary concern, but a time-consuming and tedious work was performed by radiologists or …

The segmentation, detection, and extraction of infected tumor area from magnetic resonance
(MR) images are a primary concern but a tedious and time taking task performed by …

Automation in medical industry has become one of the necessities in today's medical
scenario. Radiologists/physicians need such automation techniques for accurate diagnosis …

A Brain Tumor is essentially a malformed cell growth that can be cancerous and non-
cancerous. The tumor in the Brain is the most dangerous disease and can be diagnosed …

In clinical settings, a lot of medical image datasets suffer from the imbalance problem which
hampers the detection of outliers (rare health care events), as most classification methods …

Brain Tumor Detection is one of the most difficult tasks in medical image processing. The
detection task is difficult to perform because there is a lot of diversity in the images as brain …

A supervised CNN Deep net classifier is proposed for the detection, classification and
diagnosis of meningioma brain tumor using deep learning approach. This proposed method …

The standard one-class support vector machine (OC-SVM) is sensitive to noises, since every
instance is equally treated. To address this problem, the weighted one-class support vector …

Brain tumor and other nervous systems cancer are one of the leading causes of death for
many patients. Magnetic resonance imaging (MRI) is the most important medical imaging …

One-class classification methods are often used for anomaly detection in healthcare, quality
control in manufacturing, and fraud detection in financial services. Particularly in medical …