Identification of meningioma tumor using recurrent neural networks

By the calculations of national center for biotechnology information from COVID 19 pandemic, number of meningioma tumor patients are increasing in world. Identifying the meningioma tumor and its position in brain is not easy task by using deep neural networking based medical imaging. But it is needed to identify meningioma tumors in brain by using AI based medical imaging for the purpose of medical artificial intelligence technology innovation. Comparing to neural network results with recurrent neural network results can give accurate results. For identifying the patients’ present condition and prediction of future behavior by using recurrent neural network is need for us. Increase the accurate results for neural networking based medical imaging in health care is very expensive. By using recurrent neural networks (RNN) algorithm with many hidden layers for identification of tumor(s) in human brain with high accuracy by comparison of existing images in our data base with new unknown medical image with low cost. In this study first we are collecting the masks of skull from MRI image and dividing the masks to different types of datasets depending on age criteria like a child age, middle age and old age with two types male and female. Then we can get totally 6 types of datasets. All these masks of MRI images to binary imaging by using morphological erosion concept after that storing that masks in data sets then collect the new MRI image and comparing its mask part of skull with existing dataset in recurrent neural networks.