A numerical computer model based on the dual reciprocity boundary element method (DRBEM) is extended to study the generalized thermo elastic responses of functionally graded anisotropic rotating plates. In the case of plane deformation, a predictor-corrector implicit-explicit time integration algorithm was developed and implemented for use with the DRBEM to obtain the solution for the displacement and temperature fields in the context of the Green and Lindsay theory. Numerical results that demonstrate the validity of the proposed method are also presented in the tables.
Background: Autoimmune bullous skin dermatoses (AIBD) diagnosis relies on direct immunofluorescence examination performed on frozen tissue sections. However, this is not always available for DIF; therefore, alternative techniques needed for diagnosis. We tested the usefulness of C4d immunohistochemistry on formalin-fixed, paraffin-embedded tissue (FFPE) sections for the diagnosis of AIBD. Objective:to evaluate the role of C4d expression using immunohistochemistry in diagnosis of some autoimmune bullous skin diseases. Methods: This study included FFPE tissue blocks of 35 cases from archives of pathology department, Faculty of medicine, Zagazig University in the period from January 2017 to December 2017. These 35 cases were diagnosed histopathologically as: 30 cases autoimmune bullous dermatoses (18 pemphigus vulgaris, 6 bullous pemphigoid, 3 pemphigus foliaceus and 3 drug induced pemphigus) and 5 cases erythema multiforme. Specimens were obtained as punch biopsy from the edge of a recent bullous lesion. C4d immunostaining was performed and correlated with clinicopathology. Results:C4d immunohistochemistry was a reliable method for detecting AIBD in 29 of 30 cases diagnosed by histopathology, with 96.7% sensitivity. Also it was efficient in ruling out all the 5 negative cases ruled out by histopathology with 100% specificity. Conclusion: When correlated with the light microscopic and clinical findings, the C4d assay defines an important diagnostic adjunct in the evaluation of some autoimmune vesiculobullous dermatoses. It may prompt further DIF testing or, in some instances, may even define a reasonable substitute for DIF and/or add to the morphologic assessment of a biopsy specimen submitted for routine light microscopic assessment.
Extracting building footprint from aerial photos and satellite imagery has played a vital role in change detection, urban development, and detect the Agricultural land encroachments. The deep neural networks have feature extraction capability and provide the methods to detect and extract building footprint from Satellite imagery with high accuracy. Image segmentation, is the process by that we try to segment an image into coherent parts with two type of segmentation. Semantic segmentation is a form of segmentation that attempts to segment an image into meaningful parts or predefined class labels. The pixel-wise classification task can help us determine if a pixel be included in a particular object in a dataset. Instance segmentation is semantic segmentation with the distinction of classifying each instance of an object as itself. The convolutional neural networks (CNN) used in instance and semantic segmentation. Nevertheless, one of the main problems of extracting building footprint is that most approaches use high-resolution imagery in sampling training data and inferencing phases, whereas not free public available or available with high cost. Or use semantic segmentation that not applicable with closely situated or connected buildings. Our proposed approach is extracting building footprint low-resolution satellite imagery using the instance segmentation technique.
For women around the globe, breast cancer has been a significant cause of mortality. Around the same time, early diagnosis and high cancer prediction precision are critical to improving the quality of care and the recovery rate of the patient. Expert systems and machine learning techniques are gaining prominence in this area as a result of efficient classification and high diagnostic ability. This paper introduces a model of hybrid prediction (RS QA) based on a rough set theoryand a quasi-optimal (AQ) rule induction algorithm. To find a minimal set of attributes that completely define the results, a rough set tool is used. The selected characteristics were collected, ensuring the high standard of the classification. Then to produce the decision rules, we use the quasi-optimal (AQ) rule induction algorithm. These hybrid prediction models allow expert systems to be built based on the conceptual rules of the IF/THEN sort. The suggested experiment is performed using the Coimbra Breast Cancer Dataset (BCCD) based on sets of measures that can be obtained in routine blood tests. Using classification precision, sensitivity, specificity, and receiver operating characteristics (ROC) curves, the efficiency of our suggested approach was assessed. Experimental results indicate the highest classification accuracy (91.7 percent), sensitivity (83.3 percent), and precision (94.3) obtained by the proposed (RS_QA) model.
Breast cancer is a significant factor in female mortality. Automated identification and classification of breast histopathology image tissue characteristics using computeraided diagnostic tools is an important step in disease identification and therapy. In this work, we propose an automated classification system which is based on mixing pre-trained deep Convolutional Neural Networks (CNN) as a feature extractor, and multilevel hand-crafted features. The pre-training models are used: ResNet18, Inception ResNet v2, ShuffleNet, and Xception. These hand-crafted features are extracted using Haralick textures, Rotation and Scale-invariant Hybrid image Descriptor (RSHD), Local Diagonal Extrema Pattern (LDEP), Speeded up robust features (SURF), Colored Histogram, and the Dense Invariant Feature Transform (DSIFT) set. All extracted features are reduced by the feature selection method (PCA) and used as a feature vector for the training of three classifiers: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN). We evaluate the efficiency of the proposed methodology for public microscopy. The ICIAR-2018 dataset contains histopathology images of four classes: invasive carcinoma, in-situ carcinoma, benign tumors, and normal tissue. Experimental results show the accuracy of the proposed method at 96.97%.
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