A Hybrid Two-Stage CNN-SVM Model for Bone X-Rays Classification and Abnormality Detection

2021 ◽  
Vol 13 (4) ◽  
pp. 50-65
Author(s):  
Hadeer El-Saadawy ◽  
Manal Tantawi ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Neural Networks ◽  
Support Vector ◽  
X Rays ◽  
Abnormality Detection ◽  
Average Sensitivity ◽  
Two Stage ◽  
Second Stage ◽  
Number Of Layers ◽  
Svm Model ◽  
Detection Stage

This paper introduces a novel automatic reliable hybrid two-stage method for bone x-rays abnormality detection. For this purpose, 10 different pre-trained convolutional neural networks architectures with different number of layers are examined. The introduced method considers the seven extremity upper bones, namely shoulder, humerus, forearm, elbow, wrist, hand, and finger. The enhanced images are fed into the first stage to classify the bone type into one of the seven bones. Thereafter, the abnormality is detected in the second stage using a specific classifier according to the bone type. Thus, the classification step consists of eight different classifiers: one for the bone classification stage and seven for the abnormality detection stage. Finally, support vector machine layer is examined as a last layer of the classification in the second stage. The best average sensitivity and specificity achieved by the first stage are 95.78% and 99.45%, and 83.25% and 83.25% for the second stage, respectively. All the experiments were carried out using MURA dataset.

scholarly journals Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

2021 ◽  
Vol 13 (7) ◽  
pp. 1236
Author(s):  
Yuanjun Shu ◽  
Wei Li ◽  
Menglong Yang ◽  
Peng Cheng ◽  
Songchen Han
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Change Detection ◽  
Detection Method ◽  
Training Data ◽  
Synthetic Aperture ◽  
Learning Method ◽  
Sar Images ◽  
Two Stage ◽  
Second Stage

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

A Two-Stage Machine Learning Classification Approach to Identify Extremism in Arabic Opinions

2021 ◽  
Vol 10 (2) ◽  
pp. 736-745
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Feature Selection Method ◽  
Support Vector ◽  
Two Stage ◽  
Machine Learning Classification ◽  
Second Stage ◽  
Testing Data ◽  
Stage Classification ◽  
Positive Dataset

The increased usage of the Internet and social networks allowed and enabled people to express their views, which have generated an increasing attention lately. Sentiment Analysis (SA) techniques are used to determine the polarity of information, either positive or negative, toward a given topic, including opinions. In this research, we have introduced a machine learning approach based on Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF) classifiers, to find and classify extreme opinions in Arabic reviews. To achieve this, a dataset of 1500 Arabic reviews was collected from Google Play Store. In addition, a two-stage Classification process was applied to classify the reviews. In the first stage, we built a binary classifier to sort out positive from negative reviews. In the second stage, however we applied a binary classification mechanism based on a set of proposed rules that distinguishes extreme positive from positive reviews, and extreme negative from negative reviews. Four major experiments were conducted with a total of 10 different sub experiments to fulfill the two-stage process using different X-validation schemas and Term Frequency-Inverse Document Frequency feature selection method. Obtained results have indicated that SVM was the best during the first stage classification with 30% testing data, and NB was the best with 20% testing data. The results of the second stage classification indicated that SVM has scored better results in identifying extreme positive reviews when dealing with the positive dataset with an overall accuracy of 68.7% and NB showed better accuracy results in identifying extreme negative reviews when dealing with the negative dataset, with an overall accuracy of 72.8%.

scholarly journals A Hybrid Two-Stage GNG -Modified VGG Method for Bone X-rays Classification and Abnormality Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hadeer El-Saadawy ◽  
Manal Tantawi ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
X Rays ◽  
Abnormality Detection ◽  
Two Stage

scholarly journals Efficient Pneumonia Detection for Chest Radiography Using ResNet-Based SVM

2021 ◽  
Vol 5 (1) ◽  
pp. 1-8
Author(s):  
Marwa M. Eid ◽  
Yasser H. Elawady
Keyword(s):  
Neural Networks ◽  
Chest Radiography ◽  
Support Vector ◽  
Computational Techniques ◽  
X Rays ◽  
Automated Method ◽  
Image Investigation ◽  
Boosting Algorithm ◽  
The Individual ◽  
Hierarchical Features

Chest radiography has a significant clinical utility in the medical imaging diagnosis, as it is one of the most basic examination tools. Pneumonia is a common infection that rapidly affects human lung areas. So, finding an advanced automated method to detect Pneumonia is assigned to be one of the most recent issues, which is still prohibitively expensive to mass adoption, especially in the developing countries. This article presents an innovative approach for distinguishing the residence of pneumonia by embedding computational techniques to chest x-rays images which eliminating the demands for single-image investigation and significantly decrease the total costs. Recent advances in deep learning achieved remarkable results in image classification on different domains; however, its application for Pneumonia diagnosis is still restricted. Hence, the main focus is to provide an investigation that will improve the research in this area, presenting a new proposal to the applications of pre-trained convolutional neural networks (CNNs) as a stage of features extraction to detect this disease. Specifically, we propose to combine deep residual neural networks (ResNets), which extract the hierarchical features from the individual x-ray images with the boosting algorithm to select the salient features, and support vector machine for classification (AdaBoost-SVM). After conducting the performance analysis on the available dataset, we have concluded that the precision of the introduced scheme in Pneumonia classification is superior to the most concurrent approaches, resulting in a great improvement in clinical outcomes.

scholarly journals N-GlyDE: a two-stage N-linked glycosylation site prediction incorporating gapped dipeptides and pattern-based encoding

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Thejkiran Pitti ◽  
Ching-Tai Chen ◽  
Hsin-Nan Lin ◽  
Wai-Kok Choong ◽  
Wen-Lian Hsu ◽  
...  
Keyword(s):  
High Performance ◽  
Glycosylation Site ◽  
Support Vector ◽  
Experimental Characterization ◽  
Biological Functions ◽  
Two Stage ◽  
Post Translational Modifications ◽  
Independent Dataset ◽  
Second Stage

Abstract N-linked glycosylation is one of the predominant post-translational modifications involved in a number of biological functions. Since experimental characterization of glycosites is challenging, glycosite prediction is crucial. Several predictors have been made available and report high performance. Most of them evaluate their performance at every asparagine in protein sequences, not confined to asparagine in the N-X-S/T sequon. In this paper, we present N-GlyDE, a two-stage prediction tool trained on rigorously-constructed non-redundant datasets to predict N-linked glycosites in the human proteome. The first stage uses a protein similarity voting algorithm trained  on both glycoproteins and non-glycoproteins to predict a score for a protein to improve glycosite prediction. The second stage uses a support vector machine to predict N-linked glycosites by utilizing features of gapped dipeptides, pattern-based predicted surface accessibility, and predicted secondary structure. N-GlyDE’s final predictions are derived from a weight adjustment of the second-stage prediction results based on the first-stage prediction score. Evaluated on N-X-S/T sequons of an independent dataset comprised of 53 glycoproteins and 33 non-glycoproteins, N-GlyDE achieves an accuracy and MCC of 0.740 and 0.499, respectively, outperforming the compared tools. The N-GlyDE web server is available at http://bioapp.iis.sinica.edu.tw/N-GlyDE/.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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