ABSTRACTGlobally, a huge number of people succumb to brain tumour, which is considered to be one of the lethal types of tumours. In this research, an effective brain tumour segmentation and classification approach is ...
详细信息
ABSTRACTGlobally, a huge number of people succumb to brain tumour, which is considered to be one of the lethal types of tumours. In this research, an effective brain tumour segmentation and classification approach is implemented using Deep Learning (DL) based on Magnetic Resonance Imaging (MRI). Here, the segmentation of the tumour region from the brain image using the proposed hybrid K-Net-Deep joint segmentation (Deep K-Net), wherein the segmentation results produced by K-Net and Deep joint segmentation are combined using the Ruzicka similarity. Further, a Driving Training Taylor (DTT) algorithm is presented for training the K-Net. Classification is accomplished using the Shepard Convolutional Neural Network (ShCNN) that is optimized with the help of the proposed DTT algorithm. Further, the efficiency of the DTT_ShCNN is examined based on , accuracy, True Positive Rate (TPR), and True Negative Rate (TNR) with values of 0.936, 0.943, 0.945, and 0.949, respectively.
The cardiac right ventricle has a vital role in the cardiac cycle. To assess its function using Magnetic Resonance Imaging (MRI), the segmentation is an important task, but it is challenged by the complex shape of thi...
详细信息
The cardiac right ventricle has a vital role in the cardiac cycle. To assess its function using Magnetic Resonance Imaging (MRI), the segmentation is an important task, but it is challenged by the complex shape of this cavity, its thin borders, and shape variability. Accordingly, several approaches have been proposed to overcome these issues. Yet, a significant divergence of precision still appears among the spatial slices. In this paper, we attempt to study the impact of short-axis slices from base to apex on the segmentation process. First, a comparative study is enabled to assess the segmentation quality among these slices using a U-Net- based convolutional neural network. Two public labelled datasets are exploited with our prepared data to allow the training process. The dice-coefficient assessment of each slice-level exhibits a significant accuracy decrease for the basal and apical slices. Next, a personalized investigation is carried out for each slice level apart. Accordingly, three sub-sets are retrieved from the initial training set regrouping slices into basal, central, and apical. Furthermore, to monitor the segmentation behaviour using these sub-datasets, different U-Net-based models are trained and evaluated. The obtained results show that the central slices scores enhanced from 0.87 to 0.92 using slice-level based. On the other hand, basal and apical slices obtained higher results using the global dataset.
Differences in the structure and semantics of knowledge that is created and maintained by the various actors on the World Wide Web make its exchange and utilization a problematic task. This is an important issue facin...
详细信息
Differences in the structure and semantics of knowledge that is created and maintained by the various actors on the World Wide Web make its exchange and utilization a problematic task. This is an important issue facing organizations undertaking knowledge management initiatives. An XML-based and ontology-supported knowledge description language (KDL) is presented, which has three-tier structure (core KDL, extended KDL and complex KDL), and takes advantages of strong point of ontology, XML, description logics, frame-based systems. And then, the framework and XMLbased syntax of KDL are introduced, and the methods of translating KDL into first order logic (FOL) are presented. At last, the implementation of KDL on the Web is described, and the reasoning ability of KDL proved by experiment is illustrated in detail.
暂无评论