Nonparametric learning approach based on infinite flexible mixture model and its application to medical data analysis

The goal of this paper is to develop an effective approach allowing to capture accurately the intrinsic nature of data using an infinite shifted‐scaled Dirichlet mixture model (InSSDMM). This article extends the finite statistical model to a more efficient multidimensional infinite mixture. The flexibility of the developed framework is demonstrated via some challenging medical applications that concern diabetic retinopathy detection in eye images and pneumonia identification in chest X‐ray scans. The obtained results demonstrate the merits of such approach as compared to many other generative and discriminative models.

[1]  Bálint Antal,et al.  An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading , 2012, IEEE Transactions on Biomedical Engineering.

[2]  William D. Penny,et al.  An empirical evaluation of Bayesian sampling with hybrid Monte Carlo for training neural network classifiers , 1999, Neural Networks.

[3]  Huachun Zhou,et al.  Scene categorization towards urban tunnel traffic by image quality assessment , 2019, J. Vis. Commun. Image Represent..

[4]  Clement J. McDonald,et al.  Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration , 2014, IEEE Transactions on Medical Imaging.

[5]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection , 2020, ArXiv.

[6]  Yuan Ping,et al.  Variational Bayesian Inference for Finite Inverted Dirichlet Mixture Model and Its Application to Object Detection , 2018 .

[7]  N. Bouguila,et al.  Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images , 2020, Comput..

[8]  Bunyarit Uyyanonvara,et al.  Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods , 2008, Comput. Medical Imaging Graph..

[9]  Tony Jebara,et al.  Bhattacharyya Expected Likelihood Kernels , 2003, COLT.

[10]  Lipo Wang,et al.  Deep Learning Applications in Medical Image Analysis , 2018, IEEE Access.

[11]  U. Jha,et al.  A review of the 2019 novel coronavirus (COVID-19) based on current evidence , 2020 .

[12]  Nasir M. Rajpoot,et al.  A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[13]  Muhammad Imran Razzak,et al.  Deep Learning for Medical Image Processing: Overview, Challenges and Future , 2017, ArXiv.

[14]  P. Deb.  Finite Mixture Models , 2008 .

[15]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

[16]  Qiang Liu,et al.  Accelerating Metropolis-within-Gibbs sampler with localized computations of differential equations , 2019, Stat. Comput..

[17]  Alireza Osareh,et al.  A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images , 2009, IEEE Transactions on Information Technology in Biomedicine.

[18]  Nizar Bouguila,et al.  Generalized Gaussian mixture models as a nonparametric Bayesian approach for clustering using class-specific visual features , 2012, J. Vis. Commun. Image Represent..

[19]  Gwénolé Quellec,et al.  Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs , 2008, IEEE Transactions on Medical Imaging.

[20]  Sami Bourouis,et al.  Bayesian learning of finite generalized inverted Dirichlet mixtures: Application to object classification and forgery detection , 2014, Expert Syst. Appl..

[21]  Nizar Bouguila,et al.  Variational Bayesian inference for infinite generalized inverted Dirichlet mixtures with feature selection and its application to clustering , 2015, Applied Intelligence.

[22]  Sami Bourouis,et al.  Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification , 2019, Int. J. Imaging Syst. Technol..

[23]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[24]  Nuno Vasconcelos,et al.  A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications , 2003, NIPS.

[25]  Marleen de Bruijne,et al.  Machine learning approaches in medical image analysis: From detection to diagnosis , 2016, Medical Image Anal..

[26]  Fadi Dornaika,et al.  Learning a discriminant graph-based embedding with feature selection for image categorization , 2019, Neural Networks.

[27]  Tao Xu,et al.  An edge-region force guided active shape approach for automatic lung field detection in chest radiographs , 2012, Comput. Medical Imaging Graph..

[28]  Fatma Najar,et al.  A new hybrid discriminative/generative model using the full-covariance multivariate generalized Gaussian mixture models , 2020, Soft Comput..

[29]  Faisal R. Al-Osaimi,et al.  Bayesian inference by reversible jump MCMC for clustering based on finite generalized inverted Dirichlet mixtures , 2019, Soft Comput..

[30]  Bunyarit Uyyanonvara,et al.  Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images , 2013, Comput. Medical Imaging Graph..

[31]  Gang Song,et al.  A novel double deep ELMs ensemble system for time series forecasting , 2017, Knowl. Based Syst..

[32]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[33]  David Heckerman,et al.  Parameter Priors for Directed Acyclic Graphical Models and the Characteriration of Several Probability Distributions , 1999, UAI.

[34]  Nizar Bouguila,et al.  Model Selection and Estimation of a Finite Shifted-Scaled Dirichlet Mixture Model , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[35]  Carl E. Rasmussen,et al.  The Infinite Gaussian Mixture Model , 1999, NIPS.

[36]  Bunyarit Uyyanonvara,et al.  Machine learning approach to automatic exudate detection in retinal images from diabetic patients , 2010 .

[37]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[38]  Hossein Pourghassem,et al.  Content-based medical image classification using a new hierarchical merging scheme , 2008, Comput. Medical Imaging Graph..

[39]  Guy Cazuguel,et al.  TeleOphta: Machine learning and image processing methods for teleophthalmology , 2013 .

[40]  Joni-Kristian Kämäräinen,et al.  The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol , 2007, BMVC.

[41]  Nizar Bouguila,et al.  A hierarchical nonparametric Bayesian approach for medical images and gene expressions classification , 2014, Soft Computing.

[42]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[43]  Sami Bourouis,et al.  Deriving Probabilistic SVM Kernels From Flexible Statistical Mixture Models and its Application to Retinal Images Classification , 2019, IEEE Access.

[44]  S. Ganesan,et al.  COVID-19: A promising cure for the global panic , 2020, Science of The Total Environment.

[45]  Nizar Bouguila,et al.  Unsupervised learning of finite mixtures using scaled dirichlet distribution and its application to software modules categorization , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).

[46]  Juan M. Corchado,et al.  Visual content-based web page categorization with deep transfer learning and metric learning , 2019, Neurocomputing.

[47]  Marios S. Pattichis,et al.  Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection , 2010, IEEE Transactions on Medical Imaging.

[48]  Devi Vijayan,et al.  Detection of Exudates in Diabetic Retinopathy , 2018, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[49]  Maria Wimmer,et al.  Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs , 2017, IEEE Transactions on Medical Imaging.

[50]  N Ravia Shabnam Parveen,et al.  Detection of Pneumonia in chest X-ray images. , 2011, Journal of X-ray science and technology.

[51]  J. Olson,et al.  The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme , 2007, British Journal of Ophthalmology.

[52]  Sami Bourouis,et al.  EP-Based Infinite Inverted Dirichlet Mixture Learning: Application to Image Spam Detection , 2018, IEA/AIE.

[53]  José Manuel Bravo,et al.  A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features , 2011, IEEE Transactions on Medical Imaging.

[54]  Victor Hugo C. de Albuquerque,et al.  Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks , 2018, Neural Computing and Applications.

[55]  M. Chung,et al.  Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review , 2020, Clinical Imaging.

[56]  Antoni B. Chan,et al.  A Family of Probabilistic Kernels Based on Information Divergence , 2004 .

[57]  Radford M. Neal.  Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .

[58]  Joseph M. Reinhardt,et al.  Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images , 2013, IEEE Transactions on Medical Imaging.

[59]  Qin Li,et al.  Detection of microaneurysms using multi-scale correlation coefficients , 2010, Pattern Recognit..

[60]  Joseph Paul Cohen,et al.  COVID-19 Image Data Collection: Prospective Predictions Are the Future , 2020, ArXiv.

[61]  David Haussler,et al.  Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.

[62]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[63]  Sami Bourouis,et al.  Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection , 2019, IEEE Access.

[64]  U. Rajendra Acharya,et al.  Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages , 2008, Journal of Medical Systems.

[65]  Sami Bourouis,et al.  Infinite Scaled Dirichlet Mixture Models for Spam Filtering via Bayesian and Variational Bayes Learning , 2018, 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[66]  Yongyi Yang,et al.  Machine Learning in Medical Imaging , 2010, IEEE Signal Processing Magazine.

[67]  Unsupervised learning of finite full covariance multivariate generalized Gaussian mixture models for human activity recognition , 2018, Multimedia Tools and Applications.

[68]  Bram van Ginneken,et al.  A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays , 2015, IEEE Transactions on Medical Imaging.

[69]  N. Bouguila,et al.  Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images , 2021, J. Imaging.

[70]  Bram van Ginneken,et al.  Contextual computer-aided detection: Improving bright lesion detection in retinal images and coronary calcification identification in CT scans , 2012, Medical Image Anal..

[71]  Bram van Ginneken,et al.  Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database , 2006, Medical Image Anal..

[72]  Marleen de Bruijne.  Machine learning approaches in medical image analysis: From detection to diagnosis. , 2016, Medical image analysis.

[73]  Shuicheng Yan,et al.  A survey on deep learning-based fine-grained object classification and semantic segmentation , 2017, International Journal of Automation and Computing.

[74]  Peter F. Sharp,et al.  Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes , 2008, Diabetes Care.

[75]  Bunyarit Uyyanonvara,et al.  An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation , 2012, IEEE Transactions on Biomedical Engineering.

[76]  D. Ye,et al.  Review of the 2019 novel coronavirus (SARS-CoV-2) based on current evidence , 2020, International Journal of Antimicrobial Agents.

[77]  Benjamin Schrauwen,et al.  Factoring Variations in Natural Images with Deep Gaussian Mixture Models , 2014, NIPS.

[78]  Wei Zeng,et al.  Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer , 2017, ArXiv.