Magnetic resonance imaging (MRI) combined with artificial intelligence (AI) algorithms to detect brain tumors is one of the important medical applications. In this study, a Convolutional neural network (CNN) model proposed meningioma and pituitary, which was tested dataset consisting two categories 1,800 MRI images from several persons. The CNN trained via Python library, namely TensorFlow, an ...

purpose: to present a case of orbital granular cell tumor, a rare orbital tumor. case report: a 55-year-old woman presented with binocular diplopia and right ocular displacement. the problem had initiated 3 years ago. a firm nontender mass at the region of the right lower lid was visible. orbital ct-scan disclosed a well-defined mass in the inferior right orbit with involvement of the inferior ...

This paper is based on the research on Human Brain Tumor which uses the MRI imaging technique to capture the image. In this proposed work Brain Tumor area is calculated to define the Stage or level of seriousness of the tumor. Image Processing techniques are used for the brain tumor area calculation and Neural Network algorithms for the tumor position calculation. Also in the further advancemen...

results there was no statistical difference between the 2 groups with respect to the recurrence rate (p > 0.05). although the recurrence interval was longer for the metformin group, this increase was not statistical significant (p > 0.05). furthermore, tumor recurrence had no correlation with sex or the grade of the tumors. conclusions according to our findings, it seems that metformin has no c...

Magnetic Resonance imaging (MRI) has become a widely used method of high quality medical imaging. Brain tumor classification is one of the major problems in diagnosing the tumor at early stage. Thus various methods are surveyed in order to obtain better classification accuracy and to reduce the computational time. Since misclassification occurs due to high diversity in tumor appearance and tumo...

Neuroblastoma is an embryonic tumor derived from cells of the neural crest. Taking advantage of a newly developed neural crest lineage tracer and based on the hypothesis that the molecular mechanisms that mediate neural crest delamination are also likely to be involved in the spread of neuroblastoma, we were able to identify genes that are active both in neural crest development and neuroblasto...

Supervised and unsupervised artificial neural networks have been successfully used for image classification in biomedical applications. Supervised neural networks yield accurate classification results though the computational speed is low. On the contrary, unsupervised neural networks are comparatively faster than supervised networks besides yielding inferior classification accuracy. In this pa...

Deep learning has quickly become the weapon of choice for brain lesion segmentation. However, few existing algorithms pre-configure any biological context of their chosen segmentation tissues, and instead rely on the neural network’s optimizer to develop such associations de novo. We present a novel method for applying deep neural networks to the problem of glioma tissue segmentation that takes...