Prediction of aptamer–protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier

2019 ◽  
Vol 311 ◽  
pp. 103-108 ◽  
Author(s):  
Qing Yang ◽  
Cangzhi Jia ◽  
Taoying Li
Keyword(s):  
Feature Extraction ◽  
Ensemble Classifier ◽  
Sparse Autoencoder

scholarly journals Breath analysis based early gastric cancer classification from deep stacked sparse autoencoder neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Muhammad Aqeel Aslam ◽  
Cuili Xue ◽  
Yunsheng Chen ◽  
Amin Zhang ◽  
Manhua Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Feature Extraction ◽  
Early Gastric Cancer ◽  
Breath Analysis ◽  
Cancer Classification ◽  
Unlabeled Data ◽  
Softmax Classifier ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

AbstractDeep learning is an emerging tool, which is regularly used for disease diagnosis in the medical field. A new research direction has been developed for the detection of early-stage gastric cancer. The computer-aided diagnosis (CAD) systems reduce the mortality rate due to their effectiveness. In this study, we proposed a new method for feature extraction using a stacked sparse autoencoder to extract the discriminative features from the unlabeled data of breath samples. A Softmax classifier was then integrated to the proposed method of feature extraction, to classify gastric cancer from the breath samples. Precisely, we identified fifty peaks in each spectrum to distinguish the EGC, AGC, and healthy persons. This CAD system reduces the distance between the input and output by learning the features and preserve the structure of the input data set of breath samples. The features were extracted from the unlabeled data of the breath samples. After the completion of unsupervised training, autoencoders with Softmax classifier were cascaded to develop a deep stacked sparse autoencoder neural network. In last, fine-tuning of the developed neural network was carried out with labeled training data to make the model more reliable and repeatable. The proposed deep stacked sparse autoencoder neural network architecture exhibits excellent results, with an overall accuracy of 98.7% for advanced gastric cancer classification and 97.3% for early gastric cancer detection using breath analysis. Moreover, the developed model produces an excellent result for recall, precision, and f score value, making it suitable for clinical application.

scholarly journals Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder

2018 ◽  
Vol 12 (2) ◽  
pp. 73-84 ◽  
Author(s):  
Peng-Fei Wang ◽  
Xiao-Qing Luo ◽  
Xin-Yi Li ◽  
Zhan-Cheng Zhang
Keyword(s):  
Feature Extraction ◽  
Image Fusion ◽  
High Frequency ◽  
Extraction Method ◽  
Fusion Rule ◽  
Complex Data ◽  
Shearlet Transform ◽  
Feature Extraction Method ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. Motivated by the advantages mentioned above, a novel stacked sparse autoencoder and shift invariant shearlet transform-based image fusion method is proposed. First, the source images are decomposed into low- and high-frequency subbands by shift invariant shearlet transform; second, a two-layer stacked sparse autoencoder is adopted as a feature extraction method to get deep and sparse representation of high-frequency subbands; third, a stacked sparse autoencoder feature-based choose-max fusion rule is proposed to fuse the high-frequency subband coefficients; then, a weighted average fusion rule is adopted to merge the low-frequency subband coefficients; finally, the fused image is obtained by inverse shift invariant shearlet transform. Experimental results show the proposed method is superior to the conventional methods both in terms of subjective and objective evaluations.

scholarly journals Temporal-Spatial Neighborhood Enhanced Sparse Autoencoder for Nonlinear Dynamic Process Monitoring

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1079
Author(s):  
Nanxi Li ◽  
Hongbo Shi ◽  
Bing Song ◽  
Yang Tao
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Process Monitoring ◽  
Dynamic Process ◽  
Process Data ◽  
Detection Scheme ◽  
Current Time ◽  
Neighborhood Information ◽  
Sparse Autoencoder ◽  
Current Sample

Data-based process monitoring methods have received tremendous attention in recent years, and modern industrial process data often exhibit dynamic and nonlinear characteristics. Traditional autoencoders, such as stacked denoising autoencoders (SDAEs), have excellent nonlinear feature extraction capabilities, but they ignore the dynamic correlation between sample data. Feature extraction based on manifold learning using spatial or temporal neighbors has been widely used in dynamic process monitoring in recent years, but most of them use linear features and do not take into account the complex nonlinearities of industrial processes. Therefore, a fault detection scheme based on temporal-spatial neighborhood enhanced sparse autoencoder is proposed in this paper. Firstly, it selects the temporal neighborhood and spatial neighborhood of the sample at the current time within the time window with a certain length, the spatial similarity and time serial correlation are used for weighted reconstruction, and the reconstruction combines the current sample as the input of the sparse stack autoencoder (SSAE) to extract the correlation features between the current sample and the neighborhood information. Two statistics are constructed for fault detection. Considering that both types of neighborhood information contain spatial-temporal structural features, Bayesian fusion strategy is used to integrate the two parts of the detection results. Finally, the superiority of the method in this paper is illustrated by a numerical example and the Tennessee Eastman process.

scholarly journals Diagnosis of Melanoma Based on the Sparse Auto-Encoder for Feature Extraction

2020 ◽  
pp. 220-230
Author(s):  
Nadia Smaoui Zghal ◽  
Marwa Zaabi ◽  
Houda Derbel
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Support Vector Machine Classifier ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Suspected Case ◽  
K Nearest Neighbors ◽  
Unsupervised Deep Learning ◽  
Sparse Autoencoder ◽  
Experimental Findings

Aims: Skin cancer is a fairly critical disease all over the world and especially in Western countries and America. However, if it is perceived and treated early, it is quite often curable. The main risk factors for melanoma are exposure to UV rays, the presence of many moles, and heredity. For this reason, this work focuses on the issue of automatic diagnosis of melanoma. The aim is to extract significant features from pixels of the images based on an unsupervised deep learning technique which is the sparse autoencoder method. Methodology: A preprocessing phase is required to remove the artifacts and enhance the contrast of the images before proceeding with the feature extraction. Once the characteristics are extracted automatically, the support vector machine classifier and the k-nearest neighbors are applied for the classification phase. The objective is to differentiate between 3 categories: melanoma, suspected case, and non-melanoma. Finally, the PH2 database is used to test the proposed approaches (200 images are presented in this dataset: 80 atypical nevi, 80 common nevi, and 40 melanoma). Results: The obtained results in terms of specificity, accuracy, and sensitivity present noticeable performances with the support vector machine classifier (achieved 94 % overall accuracy) and the k-nearest neighbors (92 %). Conclusion: This study's experimental findings showed that the best performance was obtained by the approach based on a deep sparse autoencoder combined with support vector machine.

A Computer Aided System to Discriminate Enhanced Colon Images by Three Data Mining Methods

2013 ◽  
Vol 711 ◽  
pp. 636-640
Author(s):  
Ya Wen Yu ◽  
Hong Mau Lin ◽  
Bor Wen Cheng
Keyword(s):  
Feature Extraction ◽  
Image Data ◽  
Histogram Equalization ◽  
Ensemble Classifier ◽  
Computer Aided Diagnosis ◽  
Ensemble Classification ◽  
Colon Polyps ◽  
Computer Aided ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Computer-aided diagnosis for colon polyps automatically determines the locations of suspicious polyps and masses in Colonoscopy and presents them to doctors, typically as a second opinion. The proposed of Computer-aided diagnosis system consists:Using histogram equalization to do the image in the feature extraction and the classification. The researched image data were collected from a community hospital in Mid-Taiwan. First we used the histogram equalization to do the image enhancement, we got six characteristic values and calculate by the gray-scale co-occurrence matrix to get feature extraction. Finally, we used Decision Tree, Logistic Regression and ENSEMBLE to undergo colonoscopy image data classification. This researched found that difference of six texture parameter between normal and polyp group is significant. The accuracy of ENSEMBLE classification is best (90.00%). It indicates the ENSEMBLE classifier based on texture is effective for classifying polyp from tissue on colon imaging. The results of this study can be help the physician to get reliable and consistent diagnostic results and improve the quality of diagnostic imaging.

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