Convolution Neural Network Models for Acute Leukemia Diagnosis
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João Tavares | Rodrigo Veras | Justino Santos | Maíla Claro | Luis Vogado | André Santana | Vinicius Machado | J. Tavares | M. Claro | R. Veras | A. Santana | V. Machado | L. Vogado | L. H. Vogado | Justino Santos
[1] Zahid Mehmood,et al. Classification of acute lymphoblastic leukemia using deep learning , 2018, Microscopy research and technique.
[2] Samabia Tehsin,et al. Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks , 2018, Technology in cancer research & treatment.
[3] G. Travlos,et al. Normal Structure, Function, and Histology of the Bone Marrow , 2006, Toxicologic pathology.
[4] J. R. Landis,et al. The measurement of observer agreement for categorical data. , 1977, Biometrics.
[5] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[6] Hossein Rabbani,et al. Selection of the best features for leukocytes classification in blood smear microscopic images , 2014, Medical Imaging.
[7] Kosin Chamnongthai,et al. Classification of acute leukemia using medical-knowledge-based morphology and CD marker , 2018, Biomed. Signal Process. Control..
[8] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[9] Jitendra Virmani,et al. Computer assisted classification framework for prediction of acute lymphoblastic and acute myeloblastic leukemia , 2017 .
[10] Adrião Duarte Dória Neto,et al. Automatic segmentation and classification of blood components in microscopic images using a fuzzy approach , 2014 .
[11] Xin Zheng,et al. Fast and robust segmentation of white blood cell images by self-supervised learning. , 2018, Micron.
[12] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Jay S. Raval,et al. Experience with CellaVision DM96 for peripheral blood differentials in a large multi-center academic hospital system , 2012, Journal of pathology informatics.
[14] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Andrew Zisserman,et al. Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.
[16] Kittiya Khongkraphan,et al. Convolutional Neural Networks for Recognition of Lymphoblast Cell Images , 2019, Comput. Intell. Neurosci..
[17] Alireza Mehri Dehnavi,et al. Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing , 2015, Advanced biomedical research.
[18] Muhammad Imran Razzak,et al. Deep Learning for Medical Image Processing: Overview, Challenges and Future , 2017, ArXiv.
[19] Sos S. Agaian,et al. Deterministic model for Acute Myelogenous Leukemia classification , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[20] Vanika Singhal,et al. Texture Features for the Detection of Acute Lymphoblastic Leukemia , 2016 .
[21] Vincenzo Piuri,et al. All-IDB: The acute lymphoblastic leukemia image database for image processing , 2011, 2011 18th IEEE International Conference on Image Processing.
[22] Ki-Ryong Kwon,et al. Acute lymphoid leukemia classification using two-step neural network classifier , 2015, 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV).
[23] Suk-Hwan Lee,et al. Leukemia Blood Cell Image Classification Using Convolutional Neural Network , 2018 .
[24] Wisuwat Sunhem,et al. A comparison between shallow and deep architecture classifiers on small dataset , 2016, 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE).
[25] Dan Li,et al. Using Convolutional Neural Networks for Automated Fine Grained Image Classification of Acute Lymphoblastic Leukemia , 2018, 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA).
[26] Taghi M. Khoshgoftaar,et al. A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.
[27] Adil Alpkocak,et al. Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network , 2019, Diagnostics.
[28] Sos S. Agaian,et al. Automated Screening System for Acute Myelogenous Leukemia Detection in Blood Microscopic Images , 2014, IEEE Systems Journal.
[29] Wojciech Czarnecki,et al. On Loss Functions for Deep Neural Networks in Classification , 2017, ArXiv.
[30] Romuere Rôdrigues Veloso e Silva,et al. Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification , 2018, Eng. Appl. Artif. Intell..
[31] D. Goutam,et al. Classification of acute myelogenous leukemia in blood microscopic images using supervised classifier , 2015, 2015 IEEE International Conference on Engineering and Technology (ICETECH).
[32] J. Böhm. Pathologie-Websites im World Wide Web , 2007, Der Pathologe.
[33] Hossein Rabbani,et al. Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation , 2015, 2015 IEEE International Conference on Image Processing (ICIP).
[34] Yezhou Yang,et al. Convolutional Neural Networks: Ensemble Modeling, Fine-Tuning and Unsupervised Semantic Localization , 2017, J. Vis. Commun. Image Represent..
[35] Anubha Gupta,et al. SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging , 2017, MICCAI.