Hybrid models for classifying histological images: An association of deep features by transfer learning with ensemble classifier

[1]  S. El-Rabaie,et al.  Medical image enhancement algorithms using deep learning-based convolutional neural network , 2023, Journal of Optics.

[2]  Jiann-Shu Lee,et al.  Breast Tumor Tissue Image Classification Using DIU-Net , 2022, Sensors.

[3]  Wei Yan,et al.  An ensemble framework of deep neural networks for colorectal polyp classification , 2022, Multimedia Tools and Applications.

[4]  Sharda Vashisth,et al.  A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images , 2022, Comput. Biol. Medicine.

[5]  Kadir Can Burçak,et al.  A New Hybrid Breast Cancer Diagnosis Model Using Deep Learning Model and ReliefF , 2022, Traitement du Signal.

[6]  S. Behera,et al.  Automatic classification with concatenation of deep and handcrafted features of histological images for breast carcinoma diagnosis , 2022, Multimedia Tools and Applications.

[7]  Tan Wang,et al.  A novel filter feature selection algorithm based on relief , 2021, Applied Intelligence.

[8]  Leandro Alves Neves,et al.  Fractal Neural Network: A new ensemble of fractal geometry and convolutional neural networks for the classification of histology images , 2021, Expert Syst. Appl..

[9]  K. Kim,et al.  New polyp image classification technique using transfer learning of network-in-network structure in endoscopic images , 2021, Scientific Reports.

[10]  Ibrahim I. M. Manhrawy,et al.  Hybrid feature selection model based on relief‐based algorithms and regulizer algorithms for cancer classification , 2021, Concurr. Comput. Pract. Exp..

[11]  Sumit Kumar,et al.  Sub-classification of invasive and non-invasive cancer from magnification independent histopathological images using hybrid neural networks , 2021, Evolutionary Intelligence.

[12]  Constantino Carlos Reyes-Aldasoro,et al.  Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin , 2020, Cancers.

[13]  Madhu S. Nair,et al.  Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier , 2020, Biocybernetics and Biomedical Engineering.

[14]  Sanyam Shukla,et al.  Breast cancer histopathology image classification using kernelized weighted extreme learning machine , 2020, Int. J. Imaging Syst. Technol..

[15]  Ruqayya Awan,et al.  Glandular structure-guided classification of microscopic colorectal images using deep learning , 2020, Comput. Electr. Eng..

[16]  Diyar Qader Zeebaree,et al.  A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction , 2020, Journal of Applied Science and Technology Trends.

[17]  Metin N Gurcan,et al.  A modular cGAN classification framework: Application to colorectal tumor detection , 2019, Scientific Reports.

[18]  Mario Coccia,et al.  Deep Learning Technology for Improving Cancer Care in Society: New Directions in Cancer Imaging Driven by Artificial Intelligence , 2019 .

[19]  Alessandro Santana Martins,et al.  Classification of breast and colorectal tumors based on percolation of color normalized images , 2019, Comput. Graph..

[20]  Mingjiang Wang,et al.  HWDCNN: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network , 2019, Biocybernetics and Biomedical Engineering.

[21]  Zhiwen Yu,et al.  A survey on ensemble learning , 2019, Frontiers of Computer Science.

[22]  Peyman Hosseinzadeh Kassani,et al.  A comparative study of deep learning architectures on melanoma detection. , 2019, Tissue & cell.

[23]  Alessandro Santana Martins,et al.  Classification of colorectal cancer based on the association of multidimensional and multiresolution features , 2019, Expert Syst. Appl..

[24]  Moacir Antonelli Ponti,et al.  Generalization of feature embeddings transferred from different video anomaly detection domains , 2019, J. Vis. Commun. Image Represent..

[25]  Evangelia I. Zacharaki,et al.  Tensor Decomposition for Multiple-Instance Classification of High-Order Medical Data , 2018, Complex..

[26]  Mohamed Lamine Mekhalfi,et al.  Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery , 2018, Remote. Sens..

[27]  Leandro Alves Neves,et al.  Multidimensional and fuzzy sample entropy (SampEnMF) for quantifying H&E histological images of colorectal cancer , 2018, Comput. Biol. Medicine.

[28]  L. Shen,et al.  Reverse active learning based atrous DenseNet for pathological image classification , 2018, BMC Bioinformatics.

[29]  Zhang Yi,et al.  Breast cancer cell nuclei classification in histopathology images using deep neural networks , 2018, International Journal of Computer Assisted Radiology and Surgery.

[30]  Randal S. Olson,et al.  Relief-Based Feature Selection: Introduction and Review , 2017, J. Biomed. Informatics.

[31]  Catarina Eloy,et al.  Classification of breast cancer histology images using Convolutional Neural Networks , 2017, PloS one.

[32]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[33]  Takumi Kobayashi,et al.  Semi-Supervised Feature Transformation for Tissue Image Classification , 2016, PloS one.

[34]  Hao Chen,et al.  Gland segmentation in colon histology images: The glas challenge contest , 2016, Medical Image Anal..

[35]  Verónica Bolón-Canedo,et al.  A review of microarray datasets and applied feature selection methods , 2014, Inf. Sci..

[36]  Andrew Y. Ng,et al.  Regularization and feature selection in least-squares temporal difference learning , 2009, ICML '09.

[37]  Geoffrey E. Hinton Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.

[38]  Josef Kittler,et al.  Combining classifiers , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[39]  Zhihai Lu,et al.  Pathological brain detection based on AlexNet and transfer learning , 2019, J. Comput. Sci..

[40]  Zhenghao Shi,et al.  A deep CNN based transfer learning method for false positive reduction , 2018, Multimedia tools and applications.

[41]  James Geller,et al.  Data Mining: Practical Machine Learning Tools and Techniques - Book Review , 2002, SIGMOD Rec..