RESUMEN
This article introduces automatic brain tumor detection from a magnetic resonance image (MRI). It provides novel algorithms for extracting patches and segmentation trained with Convolutional Neural Network (CNN)'s to identify brain tumors. Further, this study provides deep learning and image segmentation with CNN algorithms. This contribution proposed two similar segmentation algorithms: one for the Higher Grade Gliomas (HGG) and the other for the Lower Grade Gliomas (LGG) for the brain tumor patients. The proposed algorithms (Intensity normalization, Patch extraction, Selecting the best patch, segmentation of HGG, and Segmentation of LGG) identify the gliomas and detect the stage of the tumor as per taking the MRI as input and segmented tumor from the MRIs and elaborated the four algorithms to detect HGG, and segmentation to detect the LGG works with CNN. The segmentation algorithm is compared with different existing algorithms and performs the automatic identification reasonably with high accuracy as per epochs generated with accuracy and loss curves. This article also described how transfer learning has helped extract the image and resolution of the image and increase the segmentation accuracy in the case of LGG patients.
Asunto(s)
Neoplasias Encefálicas , Glioma , Algoritmos , Encéfalo , Neoplasias Encefálicas/diagnóstico por imagen , Glioma/diagnóstico por imagen , Humanos , Procesamiento de Imagen Asistido por Computador , Imagen por Resonancia Magnética/métodos , Redes Neurales de la ComputaciónRESUMEN
The healthcare technologies in COVID-19 pandemic had grown immensely in various domains. Blockchain technology is one such turnkey technology, which is transforming the data securely; to store electronic health records (EHRs), develop deep learning algorithms, access the data, process the data between physicians and patients to access the EHRs in the form of distributed ledgers. Blockchain technology is also made to supply the data in the cloud and contact the huge amount of healthcare data, which is difficult and complex to process. As the complexity in the analysis of data is increasing day by day, it has become essential to minimize the risk of data complexity. This paper supports deep neural network (DNN) analysis in healthcare and COVID-19 pandemic and gives the smart contract procedure, to identify the feature extracted data (FED) from the existing data. At the same time, the innovation will be useful to analyse future diseases. The proposed method also analyze the existing diseases which had been reported and it is extremely useful to guide physicians in providing appropriate treatment and save lives. To achieve this, the massive data is integrated using Python scripting language under various libraries to perform a wide range of medical and healthcare functions to infer knowledge that assists in the diagnosis of major diseases such as heart disease, blood cancer, gastric and COVID-19.