Brain tumor segmentation plays an essential role in medical image analysis. In recent studies, deep convolution neural networks (DCNNs) are extremely powerful to tackle tasks. We propose this paper a novel training method that enhances the results by adding additional classification branch network. The whole network was trained end-to-end on Multimodal Tumor Segmentation Challenge (BraTS) 2020 ...

Probabilistic Neural Network (PNN) also termed to be a learning machine is preliminarily used with an extension of various image classifications based on Training networks and Testing networks. To efficiently detect Brain Tumor cells, clustering method based on FCM can also be implemented. The Probabilistic Neural Network (PNN) will be employed to classify the various stages of Tumor cut levels...

Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early stages is of significant importance to devise a precise treatment plan and predict patient’s response to the adopted treatment. In this regard, there has been a recent surge of interest in designing Convolutional Neur...

Current research in biomedical informatics involves analysis of multiple heterogeneous data sets. This includes patient demographics, clinical and pathology data, treatment history, patient outcomes as well as gene expression, DNA sequences and other information sources such as gene ontology. Analysis of these data sets could lead to better diseas...

BACKGROUND. Findings of isolated (disseminated or circulating) tumor cells (ITC) by immunocytochemistry and molecular pathology methods have led to varied interpretations and different applications of the TNM system. METHODS. An analysis of the relevant literature was undertaken. In addition, optional proposals for the classification of ITC, micrometastasis, and cytologic results in pleural and...

Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We ob...

In this study, a method for classification of tumor sample microarray data based on support vector machines is presented. Different possibilities for data processing, gene selection and support vector machine classification are recited. The performance of support vector machine classification is compared to that of linear discriminant analysis and decision tree -based classifiers.

This paper proposes a new method for tumor classification using gene expression data, which mainly contains three steps. Firstly, the original DNA microarray gene expression data are selected using t-statistics. Secondly, the selected genes are modeled by Independent Component Analysis (ICA). Finally, Support Vector Machine (SVM) is used to classify the modeling data. To show the validity of th...

Today world the brain tumor is life threatening and the main reason for the death. The growth of abnormal cells in brain leads to brain tumor. Brain tumor is categorized into malignant tumor and benign tumor. Malignant is cancerous whereas Benign tumor is non-cancerous. Diagnosing at earlier stage can save the person. It is actually a great challenge to find the brain tumor and classifying its ...

It is of great importance to classify the gene expression data into different classes. In this paper, followed the assumption that the gene expression data of tumor may be sampled from the data with a probability distribution on a sub-manifold of ambient space, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM), is presented for tumor classification. The...