Simple tricks of convolutional neural network architectures improve DNA–protein binding prediction

2018 ◽  
Vol 35 (11) ◽  
pp. 1837-1843 ◽  
Author(s):  
Zhen Cao ◽  
Shihua Zhang
Keyword(s):  
Neural Network ◽  
Protein Binding ◽  
Convolutional Neural Network ◽  
Network Architectures ◽  
Binding Prediction ◽  
Neural Network Architectures

scholarly journals Convolutional neural network architectures for predicting DNA–protein binding

2016 ◽  
Vol 32 (12) ◽  
pp. i121-i127 ◽  
Author(s):  
Haoyang Zeng ◽  
Matthew D. Edwards ◽  
Ge Liu ◽  
David K. Gifford
Keyword(s):  
Neural Network ◽  
Protein Binding ◽  
Convolutional Neural Network ◽  
Network Architectures ◽  
Neural Network Architectures

scholarly journals Calcification detection using convolutional neural network architectures in Intravascular ultrasound images

2020 ◽  
Vol 17 (3) ◽  
pp. 1313
Author(s):  
Hannah Sofian ◽  
Joel Than Chia Ming ◽  
Suraya Muhammad ◽  
Norliza Mohd Noor
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Coronary Artery ◽  
Convolutional Neural Network ◽  
Intravascular Ultrasound ◽  
Predictive Value ◽  
Area Under Curve ◽  
Ultrasound Image ◽  
Network Architectures ◽  
Neural Network Architectures

<p>Cardiovascular disease is the highest leading to death for Non-Communicable disease. Coronary artery calcification disease is part of cardiovascular disease. The built-in of the plaques and the calcification in the coronary artery inner wall make the blood vessel cross-section area narrow. The standard practice by the radiologists and medical clinical are by visual inspection to detect the calcification in the intravascular ultrasound image. Deep learning is the current image processing methods that have high potential to detect calcification analysis using convolutional neural network architecture and classifiers. To detect the absence of calcification and presence calcification on the intravascular ultrasound image, using k-fold =10, we compared the three types of convolutional neural network architectures and the seven types of classifiers with the provided ground truth from MICCAI 2011. We used two types of images named as Cartesian Coordinates image and polar reconstructed coordinate image. The classifiers such as Support Vector Machine, Discriminant analysis, Ensembles and Error-Correcting Output Codes obtained the perfect result with value one for Area Under Curve and all the performance measure result, accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Area Under Curve for Naïve Bayes classifier is 0.9967 and for Decision Tree classifier is 0.9994, obtained using the polar reconstructed coordinate image for InceptionresNet-V2 architecture.</p>

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