Image classification method on class imbalance datasets using multi-scale CNN and two-stage transfer learning

[1]  Eyad Elyan,et al.  MFC-GAN: Class-imbalanced dataset classification using Multiple Fake Class Generative Adversarial Network , 2019, Neurocomputing.

[2]  Ömer Faruk Arar,et al.  Software defect prediction using cost-sensitive neural network , 2015, Appl. Soft Comput..

[3]  Dazhe Zhao,et al.  Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm , 2017, Comput. Medical Imaging Graph..

[4]  Cheng Shi,et al.  Adaptive multi-scale deep neural networks with perceptual loss for panchromatic and multispectral images classification , 2019, Inf. Sci..

[5]  Atsuto Maki,et al.  A systematic study of the class imbalance problem in convolutional neural networks , 2017, Neural Networks.

[6]  Bernd Scholz-Reiter,et al.  Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection , 2016 .

[7]  Hichem Snoussi,et al.  A fast and robust convolutional neural network-based defect detection model in product quality control , 2017, The International Journal of Advanced Manufacturing Technology.

[8]  Lijun Xie,et al.  A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data , 2018, Pattern Recognit..

[9]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[10]  Yu Guo,et al.  A Two-Stage Transfer Learning-Based Deep Learning Approach for Production Progress Prediction in IoT-Enabled Manufacturing , 2019, IEEE Internet of Things Journal.

[11]  Naomi S. Altman,et al.  Points of Significance: Classification evaluation , 2016, Nature Methods.

[12]  Q. M. Jonathan Wu,et al.  Salient object detection via multi-scale attention CNN , 2018, Neurocomputing.

[13]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[14]  Xiaofei Zhou,et al.  Two-Stage Transfer Learning of End-to-End Convolutional Neural Networks for Webpage Saliency Prediction , 2017, IScIDE.

[15]  Kenli Li,et al.  Multi-task cascade deep convolutional neural networks for large-scale commodity recognition , 2019, Neural Computing and Applications.

[16]  Yanyun Tao,et al.  Evolutionary synthetic oversampling technique and cocktail ensemble model for warfarin dose prediction with imbalanced data , 2021, Neural Comput. Appl..

[17]  Ekrem Duman,et al.  A cost-sensitive decision tree approach for fraud detection , 2013, Expert Syst. Appl..

[18]  Ales Procházka,et al.  Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation , 2016, IEEE Transactions on Image Processing.

[19]  Aleš Procházka,et al.  Multi-Class Sleep Stage Analysis and AdaptivePattern Recognition , 2018 .

[20]  Dongrui Wu,et al.  On the Vulnerability of CNN Classifiers in EEG-Based BCIs , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  Jan Švihlík,et al.  Biomedical Image Volumes Denoising via the Wavelet Transform , 2011 .

[22]  Mehedi Masud,et al.  Convolutional neural network-based models for diagnosis of breast cancer , 2020, Neural computing & applications.

[23]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[24]  Juan José Rodríguez Diez,et al.  Diversity techniques improve the performance of the best imbalance learning ensembles , 2015, Inf. Sci..

[25]  Li Dong,et al.  Crowd counting by using multi-level density-based spatial information: A Multi-scale CNN framework , 2020, Inf. Sci..

[26]  Sinan Uğuz,et al.  Classification of olive leaf diseases using deep convolutional neural networks , 2020, Neural Computing and Applications.

[27]  Aditya Desai,et al.  An efficient neural-network model for real-time fault detection in industrial machine , 2020, Neural Computing and Applications.

[28]  Dongrui Wu,et al.  Optimize TSK Fuzzy Systems for Classification Problems: Minibatch Gradient Descent With Uniform Regularization and Batch Normalization , 2020, IEEE Transactions on Fuzzy Systems.

[29]  B. Uma Maheswari,et al.  HPWO-LS-based deep learning approach with S-ROA-optimized optic cup segmentation for fundus image classification , 2021, Neural Computing and Applications.