Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
暂无分享,去创建一个
Abdelmgeid A. Ali | Ponnuthurai N. Suganthan | Essam H. Houssein | Marwa M. Emam | P. Suganthan | E. H. Houssein
[1] René V. Mayorga,et al. An automated confirmatory system for analysis of mammograms , 2016, Comput. Methods Programs Biomed..
[2] Muhammad Sharif,et al. Stomach Deformities Recognition Using Rank-Based Deep Features Selection , 2019, Journal of Medical Systems.
[3] Gustavo Carneiro,et al. The Automated Learning of Deep Features for Breast Mass Classification from Mammograms , 2016, MICCAI.
[4] Luiz Eduardo Soares de Oliveira,et al. Breast cancer histopathological image classification using Convolutional Neural Networks , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[5] Inas A. Yassine,et al. Novel features for microcalcification detection in digital mammogram images based on wavelet and statistical analysis , 2015, 2015 IEEE International Conference on Image Processing (ICIP).
[6] Yuliant Sibaroni,et al. Implementation of decision tree using C4.5 algorithm in decision making of loan application by debtor (Case study: Bank pasar of Yogyakarta Special Region) , 2015, 2015 3rd International Conference on Information and Communication Technology (ICoICT).
[7] Ron Kimmel,et al. Computational mammography using deep neural networks , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..
[8] Pritee Khanna,et al. Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM , 2015, Journal of Digital Imaging.
[9] Yi Guo,et al. Robust phase-based texture descriptor for classification of breast ultrasound images , 2015, BioMedical Engineering OnLine.
[10] D. Shen,et al. Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans , 2016, Scientific Reports.
[11] Wei-Yin Loh,et al. A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms , 2000, Machine Learning.
[12] Pavel Kisilev,et al. Medical Image Description Using Multi-task-loss CNN , 2016, LABELS/DLMIA@MICCAI.
[13] Yan Tong,et al. Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition , 2017, NIPS.
[14] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[15] Sang Won Yoon,et al. Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms , 2014, Expert Syst. Appl..
[16] Rahimeh Rouhi,et al. Classification of benign and malignant breast tumors based on hybrid level set segmentation , 2016, Expert Syst. Appl..
[17] Payam Amini,et al. Prediction of survival and metastasis in breast cancer patients using machine learning classifiers , 2019, Clinical Epidemiology and Global Health.
[18] Maryellen L. Giger,et al. Breast image feature learning with adaptive deconvolutional networks , 2012, Medical Imaging.
[19] Mehrbakhsh Nilashi,et al. A knowledge-based system for breast cancer classification using fuzzy logic method , 2017, Telematics Informatics.
[20] Walid Barhoumi,et al. Deep learning and non-negative matrix factorization in recognition of mammograms , 2017, International Conference on Graphic and Image Processing.
[21] Michael Galperin,et al. Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion , 2019, Medical physics.
[22] Priti P. Rege,et al. Fusion of local and global features for classification of abnormality in mammograms , 2016 .
[23] Andrzej Nowicki,et al. Combining Nakagami imaging and convolutional neural networks for breast lesion classification , 2017, 2017 IEEE International Ultrasonics Symposium (IUS).
[24] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[25] James Geller,et al. Data Mining: Practical Machine Learning Tools and Techniques - Book Review , 2002, SIGMOD Rec..
[26] A. Beigzadeh,et al. Machine learning models in breast cancer survival prediction. , 2016, Technology and health care : official journal of the European Society for Engineering and Medicine.
[27] Nidal S. Kamel,et al. Mammogram classification using dynamic time warping , 2018, Multimedia Tools and Applications.
[28] Shadi Albarqouni,et al. AggNet : Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images , 2016 .
[29] Mathieu Lamard,et al. Multiple-Instance Learning for Anomaly Detection in Digital Mammography , 2016, IEEE Transactions on Medical Imaging.
[30] Olavi Stenroos,et al. Object detection from images using convolutional neural networks , 2017 .
[31] Ju Jia Zou,et al. Adapting fisher vectors for histopathology image classification , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).
[32] Manasi Gyanchandani,et al. Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review. , 2019, Journal of medical imaging and radiation sciences.
[33] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[34] Aleksandar Peulic,et al. Thermography based breast cancer detection using texture features and minimum variance quantization , 2014, EXCLI journal.
[35] Yaxian Ma,et al. Corrigendum: Irisin promotes osteoblast proliferation and differentiation via activating the MAP kinase signaling pathways , 2016, Scientific Reports.
[36] Parashkev Nachev,et al. Computer Methods and Programs in Biomedicine NiftyNet: a deep-learning platform for medical imaging , 2022 .
[37] H. A. Kader,et al. No . 1 January 2018 PP . 12 – 32 AUTOMATIC IMAGE SEGMENTATION METHOD FOR BREAST CANCER ANALYSIS USING THERMOGRAPHY , 2018 .
[38] Arnau Oliver,et al. A review of automatic mass detection and segmentation in mammographic images , 2010, Medical Image Anal..
[39] Ya-Wen Yu,et al. Construction the Model on the Breast Cancer Survival Analysis Use Support Vector Machine, Logistic Regression and Decision Tree , 2014, Journal of Medical Systems.
[40] Alessandro Santana Martins,et al. LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues , 2016, Expert Syst. Appl..
[41] Qinghua Huang,et al. An approach based on biclustering and neural network for classification of lesions in breast ultrasound , 2016, 2016 International Conference on Advanced Robotics and Mechatronics (ICARM).
[42] Lionel Tarassenko,et al. Neural network models for breast cancer prognosis , 2005, Neural Computing & Applications.
[43] Peerapon Vateekul,et al. Combining deep convolutional networks and SVMs for mass detection on digital mammograms , 2016, 2016 8th International Conference on Knowledge and Smart Technology (KST).
[44] Hui Li,et al. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks , 2016, Journal of medical imaging.
[45] Mengjie Zhang,et al. Evolutionary algorithms for classification of mammographie densities using local binary patterns and statistical features , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).
[46] Masrah Azrifah Azmi Murad,et al. An integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification , 2017, Neural Computing and Applications.
[47] Aruna Tiwari,et al. Breast cancer diagnosis using Genetically Optimized Neural Network model , 2015, Expert Syst. Appl..
[48] Hua Li,et al. Benign and malignant classification of mammogram images based on deep learning , 2019, Biomed. Signal Process. Control..
[49] J. Ross Quinlan,et al. Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..
[50] Dongfeng Zhou,et al. A Benign and Malignant Mass Classification Algorithm Based on an Improved Level Set Segmentation and Texture Feature Analysis , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.
[51] Gustavo Carneiro,et al. A deep learning approach for the analysis of masses in mammograms with minimal user intervention , 2017, Medical Image Anal..
[52] Gustavo Carneiro,et al. Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests , 2015, 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA).
[53] M.N.S. Swamy,et al. An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine , 2020, Appl. Soft Comput..
[54] Jianzhong Wu,et al. Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology , 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).
[55] Zafer Cömert,et al. BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer , 2020 .
[56] Frans Coenen,et al. One-class kernel subspace ensemble for medical image classification , 2014, EURASIP Journal on Advances in Signal Processing.
[57] Michal Byra,et al. Combining Nakagami imaging and convolutional neural network for breast lesion classification , 2017, 2017 IEEE International Ultrasonics Symposium (IUS).
[58] Hao Chen,et al. Mitosis Detection in Breast Cancer Histology Images via Deep Cascaded Networks , 2016, AAAI.
[59] Hyo-Eun Kim,et al. Self-Transfer Learning for Weakly Supervised Lesion Localization , 2016, MICCAI.
[60] A fast and adaptive automated disease diagnosis method with an innovative neural network model , 2012, Neural Networks.
[61] Ruey-Feng Chang,et al. Feasibility Testing: Three-dimensional Tumor Mapping in Different Orientations of Automated Breast Ultrasound. , 2016, Ultrasound in medicine & biology.
[62] Daniel L. Rubin,et al. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.
[63] Bin Yan,et al. Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms , 2019, Comput. Medical Imaging Graph..
[64] Amir Hussain,et al. Local energy-based shape histogram feature extraction technique for breast cancer diagnosis , 2015, Expert Syst. Appl..
[65] Samuel Cheng,et al. An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology , 2016, SPIE Medical Imaging.
[66] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[67] Mohamed El Aroussi,et al. A comparison of multi-resolution and multi-orientation for breast cancer diagnosis in the full-field digital mammogram , 2015, 2015 27th International Conference on Microelectronics (ICM).
[68] Jing Zhao,et al. Image classification toward breast cancer using deeply-learned quality features , 2019, J. Vis. Commun. Image Represent..
[69] Leonid Karlinsky,et al. A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography , 2016, LABELS/DLMIA@MICCAI.
[70] Xiaohui Xie,et al. Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification , 2018, ICIAR.
[71] Makoto Yoshizawa,et al. Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis , 2016, 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).
[72] Anselmo Cardoso de Paiva,et al. Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM , 2015, Comput. Biol. Medicine.
[73] Badal Soni,et al. Breast cancer detection by leveraging Machine Learning , 2020, ICT Express.
[74] Nisreen I. R. Yassin,et al. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review , 2018, Comput. Methods Programs Biomed..
[75] Muhammad Hussain,et al. A comparison of different Gabor feature extraction approaches for mass classification in mammography , 2015, Multimedia Tools and Applications.
[76] Ian W. Ricketts,et al. The Mammographic Image Analysis Society digital mammogram database , 1994 .
[77] Arnaldo de Albuquerque Araújo,et al. Toward a standard reference database for computer-aided mammography , 2008, SPIE Medical Imaging.
[78] Hongyu Wang,et al. A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI. , 2020, Magnetic resonance imaging.
[79] Tae-Seong Kim,et al. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification , 2018, Int. J. Medical Informatics.
[80] Chien-Hsing Chen,et al. A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection , 2014, Appl. Soft Comput..
[81] Jaime S. Cardoso,et al. INbreast: toward a full-field digital mammographic database. , 2012, Academic radiology.
[82] Wei Wang,et al. A new fuzzy Gaussian mixture model (FGMM) based algorithm for mammography tumor image classification , 2017, Multimedia Tools and Applications.
[83] Daniel Sánchez-Ruiz,et al. Automatic region of interest segmentation for breast thermogram image classification , 2020, Pattern Recognit. Lett..
[84] Nenad Filipovic,et al. Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms. , 2015, Technology and health care : official journal of the European Society for Engineering and Medicine.
[85] Dimitrios I. Fotiadis,et al. Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.
[86] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[87] A. Alavudeen Basha,et al. Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform , 2019, Measurement.
[88] Marcus C. Araújo,et al. Interval symbolic feature extraction for thermography breast cancer detection , 2014, Expert Syst. Appl..
[89] Zhigang Zeng,et al. A new automatic mass detection method for breast cancer with false positive reduction , 2015, Neurocomputing.
[90] Gustavo Carneiro,et al. Deep structured learning for mass segmentation from mammograms , 2015, 2015 IEEE International Conference on Image Processing (ICIP).
[91] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[92] Chougrad Hiba,et al. An improved breast tissue density classification framework using bag of features model , 2016, 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt).
[93] Luqman Mahmood Mina,et al. Breast abnormality detection in mammograms using Artificial Neural Network , 2015, 2015 International Conference on Computer, Communications, and Control Technology (I4CT).
[94] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[95] Woo Kyung Moon,et al. An Artificial Immune System-Based Support Vector Machine Approach for Classifying Ultrasound Breast Tumor Images , 2015, Journal of Digital Imaging.
[96] Xinbo Gao,et al. A parasitic metric learning net for breast mass classification based on mammography , 2018, Pattern Recognit..
[97] Anne L. Martel,et al. Improving the Accuracy of Computer-aided Diagnosis for Breast MR Imaging by Differentiating between Mass and Nonmass Lesions. , 2016, Radiology.
[98] Miguel Ángel Guevara-López,et al. Representation learning for mammography mass lesion classification with convolutional neural networks , 2016, Comput. Methods Programs Biomed..
[99] Linda G. Shapiro,et al. Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks , 2018, Pattern Recognit..
[100] Ainuddin Wahid Abdul Wahab,et al. Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges , 2019, Artificial Intelligence Review.
[101] Richard H. Moore,et al. THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .
[102] Kok-Swee Sim,et al. Convolutional neural network improvement for breast cancer classification , 2019, Expert Syst. Appl..
[103] Gokhan Bilgin,et al. Mitosis detection using convolutional neural network based features , 2016, 2016 IEEE 17th International Symposium on Computational Intelligence and Informatics (CINTI).
[104] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[105] Aboul Ella Hassanien,et al. A BA-based algorithm for parameter optimization of Support Vector Machine , 2017, Pattern Recognit. Lett..
[106] Zhang Yi,et al. Automated diagnosis of breast ultrasonography images using deep neural networks , 2019, Medical Image Anal..
[107] Goreti Marreiros,et al. Applying Data Mining Techniques to Improve Breast Cancer Diagnosis , 2016, Journal of Medical Systems.
[108] Viksit Kumar,et al. Automated and real-time segmentation of suspicious breast masses using convolutional neural network , 2018, PloS one.
[109] Harris Drucker,et al. Support vector machines for spam categorization , 1999, IEEE Trans. Neural Networks.
[110] Michael S. Lew,et al. Deep learning for visual understanding: A review , 2016, Neurocomputing.
[111] Kemal Polat,et al. Breast cancer diagnosis using least square support vector machine , 2007, Digit. Signal Process..
[112] Richa Singh,et al. Improving biometric recognition accuracy and robustness using DWT and SVM watermarking , 2005, IEICE Electron. Express.
[113] Xuelong Li,et al. Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis , 2015, Inf. Sci..
[114] Zhang Xiong,et al. Convolutional Neural Network based sentiment analysis using Adaboost combination , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[115] Laimeche Lakhdar,et al. The power laws: Zipf and inverse Zipf for automated segmentation and classification of masses within mammograms , 2015, Evol. Syst..
[116] Nikos Grammalidis,et al. Grading of invasive breast carcinoma through Grassmannian VLAD encoding , 2017, PloS one.
[117] Santosh S Venkatesh,et al. Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis. , 2015, Ultrasound in medicine & biology.
[118] Lihua Li,et al. A new quantitative image analysis method for improving breast cancer diagnosis using DCE-MRI examinations. , 2014, Medical physics.
[119] Jacques Wainer,et al. Automatic breast density classification using a convolutional neural network architecture search procedure , 2015, Medical Imaging.
[120] Kai Zhang,et al. Deep learning for image-based cancer detection and diagnosis - A survey , 2018, Pattern Recognit..
[121] Inas A. Yassine,et al. Spectral correlation analysis for microcalcification detection in digital mammogram images , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).
[122] Wenqing Sun,et al. Computerized breast cancer analysis system using three stage semi-supervised learning method , 2016, Comput. Methods Programs Biomed..
[123] Kadayanallur Mahadevan Prabusankarlal,et al. Assessment of combined textural and morphological features for diagnosis of breast masses in ultrasound , 2015, Human-centric Computing and Information Sciences.
[124] Ryohei Nakayama,et al. The usefulness of a computer-aided diagnosis scheme for improving the performance of clinicians to diagnose non-mass lesions on breast ultrasonographic images , 2016, Journal of Medical Ultrasonics.
[125] Asifullah Khan,et al. Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection , 2017, Comput. Biol. Medicine.
[126] Kemal Polat,et al. A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis , 2007, Comput. Biol. Medicine.
[127] Volodymyr Ponomaryov,et al. Computer-aided detection system based on PCA/SVM for diagnosis of breast cancer lesions , 2015, 2015 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON).
[128] Dayou Liu,et al. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis , 2011, Expert Syst. Appl..
[129] Wei Hu,et al. Microcalcification detection in full-field digital mammograms with PFCM clustering and weighted SVM-based method , 2015, EURASIP J. Adv. Signal Process..
[130] Max A. Viergever,et al. Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.
[131] Nilanjan Dey,et al. Abdominal Imaging in Clinical Applications: Computer Aided Diagnosis Approaches , 2016 .
[132] Alexander,et al. Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural Network , 2015 .
[133] Mohamed Abdel-Nasser,et al. Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern , 2015, Expert Syst. Appl..
[134] Li Lan,et al. Potential of computer‐aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions , 2014, Journal of magnetic resonance imaging : JMRI.
[135] Ke Zhou,et al. Breast mass detection from the digitized X-ray mammograms based on the combination of deep active learning and self-paced learning , 2019, Future Gener. Comput. Syst..
[136] Mokhtar Sellami,et al. CAD system for classification of mammographic abnormalities using transductive semi supervised learning algorithm and heterogeneous features , 2015, 2015 12th International Symposium on Programming and Systems (ISPS).
[137] U. Rajendra Acharya,et al. Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine , 2012, Journal of Medical Systems.
[138] Edwin Valarezo,et al. Simultaneous Detection and Classification of Breast Masses in Digital Mammograms via a Deep Learning YOLO-based CAD System , 2018, Comput. Methods Programs Biomed..
[139] Anke Meyer-Bäse,et al. Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique , 2013, EURASIP J. Adv. Signal Process..
[140] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[141] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[142] Razieh Sheikhpour,et al. Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer , 2016, Appl. Soft Comput..
[143] Fabio A. González,et al. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks , 2014, Medical Imaging.
[144] Reyer Zwiggelaar,et al. Deep learning in mammography and breast histology, an overview and future trends , 2018, Medical Image Anal..
[145] Anselmo Cardoso de Paiva,et al. Automatic Detection of Masses in Mammograms Using Quality Threshold Clustering, Correlogram Function, and SVM , 2015, Journal of Digital Imaging.
[146] Yanning Zhang,et al. EMS-Net: Ensemble of Multiscale Convolutional Neural Networks for Classification of Breast Cancer Histology Images , 2019, Neurocomputing.
[147] Mita Nasipuri,et al. Patch-based system for Classification of Breast Histology images using deep learning , 2019, Comput. Medical Imaging Graph..
[148] Thomas M. Deserno,et al. Fundamentals of Biomedical Image Processing , 2010 .
[149] Sang Won Yoon,et al. A support vector machine-based ensemble algorithm for breast cancer diagnosis , 2017, Eur. J. Oper. Res..
[150] Yasser M. Kadah,et al. Implementation of practical computer aided diagnosis system for classification of masses in digital mammograms , 2015, 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE).
[151] Nico Karssemeijer,et al. Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring , 2016, IEEE Transactions on Medical Imaging.
[152] M. Usman Akram,et al. Decision support system for detection of hypertensive retinopathy using arteriovenous ratio , 2018, Artif. Intell. Medicine.
[153] Xueding Wang,et al. Medical breast ultrasound image segmentation by machine learning , 2019, Ultrasonics.
[154] A. Madabhushi,et al. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study. , 2014, Radiology.
[155] Joel Quintanilla-Domínguez,et al. WBCD breast cancer database classification applying artificial metaplasticity neural network , 2011, Expert Syst. Appl..
[156] Juan Shan,et al. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods. , 2016, Ultrasound in medicine & biology.
[157] Antonio Moreno,et al. Breast tumor classification in ultrasound images using texture analysis and super-resolution methods , 2017, Eng. Appl. Artif. Intell..
[158] Jean-Pierre Doucet,et al. Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design , 2007 .
[159] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[160] Jeon-Hor Chen,et al. Computerized Breast Mass Detection Using Multi-Scale Hessian-Based Analysis for Dynamic Contrast-Enhanced MRI , 2014, Journal of Digital Imaging.
[161] Xiaoming Liu,et al. Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method , 2014, IEEE Systems Journal.
[162] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[163] Rita de Cássia Fernandes de Lima,et al. Breast cancer diagnosis based on mammary thermography and extreme learning machines , 2018 .
[164] Nico Karssemeijer,et al. Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..
[165] Mita Nasipuri,et al. Wavelet based thermogram analysis for breast cancer detection , 2015, 2015 International Symposium on Advanced Computing and Communication (ISACC).
[166] Jeon-Hor Chen,et al. Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses. , 2015, Ultrasound in medicine & biology.
[167] Pendar Alirezazadeh,et al. Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images , 2018 .
[168] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[169] V. Rajinikanth,et al. Entropy based segmentation of tumor from brain MR images - a study with teaching learning based optimization , 2017, Pattern Recognit. Lett..
[170] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[171] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[172] Ezzeddine Zagrouba,et al. Breast cancer diagnosis in digitized mammograms using curvelet moments , 2015, Comput. Biol. Medicine.
[173] Idil Isikli Esener,et al. A new ensemble of features for breast cancer diagnosis , 2015, 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).
[174] Shabana Urooj,et al. An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier , 2016, Journal of Medical Systems.
[175] Aboul Ella Hassanien,et al. Bio-inspired Swarm Techniques for Thermogram Breast Cancer Detection , 2016 .
[176] István Csabai,et al. Detecting and classifying lesions in mammograms with Deep Learning , 2017, Scientific Reports.
[177] Rudolf Kruse,et al. Obtaining interpretable fuzzy classification rules from medical data , 1999, Artif. Intell. Medicine.
[178] Salvatore Ruggieri,et al. Efficient C4.5 , 2002, IEEE Trans. Knowl. Data Eng..
[179] Belal Al-Khateeb,et al. Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images , 2018, Comput. Electr. Eng..
[180] J. Dheeba,et al. Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach , 2014, J. Biomed. Informatics.
[181] Maryellen L Giger,et al. Residual analysis of the water resonance signal in breast lesions imaged with high spectral and spatial resolution (HiSS) MRI: a pilot study. , 2014, Medical physics.
[182] Gisbert Schneider,et al. Support vector machine applications in bioinformatics. , 2003, Applied bioinformatics.
[183] Manuela Pereira,et al. Classification of Breast Masses on Contrast-Enhanced Magnetic Resonance Images Through Log Detrended Fluctuation Cumulant-Based Multifractal Analysis , 2014, IEEE Systems Journal.
[184] Anjan Gudigar,et al. Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images , 2016, Appl. Soft Comput..
[185] J. Anitha,et al. A wavelet based morphological mass detection and classification in mammograms , 2012, 2012 International Conference on Machine Vision and Image Processing (MVIP).
[186] Prabhpreet Kaur,et al. Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification , 2019, Informatics in Medicine Unlocked.
[187] Yunsong Li,et al. Breast mass classification in digital mammography based on extreme learning machine , 2016, Neurocomputing.
[188] Çagri Cabioglu,et al. Computer-Aided Breast Cancer Diagnosis from Thermal Images Using Transfer Learning , 2020, IWBBIO.
[189] Paul Dufort,et al. A Computerized System to Assess Axillary Lymph Node Malignancy from Sonographic Images. , 2015, Ultrasound in medicine & biology.
[190] Bailing Zhang,et al. Breast cancer diagnosis from biopsy images by serial fusion of Random Subspace ensembles , 2011, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI).
[191] O. Mangasarian,et al. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. , 1990, Proceedings of the National Academy of Sciences of the United States of America.
[192] Chuanbo Guo,et al. Impacts of hatchery-reared mandarin fish Siniperca chuatsi stocking on wild fish community and water quality in a shallow Yangtze lake , 2018, Scientific Reports.
[193] Fa Zhang,et al. Breast cancer histopathological image classification using a hybrid deep neural network. , 2020, Methods.
[194] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[195] Ahmad Faraahi,et al. A novel memetic feature selection algorithm , 2013, The 5th Conference on Information and Knowledge Technology.
[196] Elif Derya íbeyli. Implementing automated diagnostic systems for breast cancer detection , 2007 .
[197] Tae-Seong Kim,et al. Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms , 2020, Comput. Methods Programs Biomed..
[198] Rita de Cássia Fernandes de Lima,et al. Deep-wavelet neural networks for breast cancer early diagnosis using mammary termographies , 2020 .
[199] Hong Liu,et al. A new approach to develop computer-aided detection schemes of digital mammograms , 2015, Physics in medicine and biology.
[200] Ruey-Feng Chang,et al. Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features. , 2015, Medical physics.
[201] Mai S. Mabrouk,et al. Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques , 2019, Ain Shams Engineering Journal.
[202] Sami Ekici,et al. Breast cancer diagnosis using thermography and convolutional neural networks. , 2019, Medical hypotheses.
[203] Abdulkadir Sengür,et al. Evaluation of ensemble methods for diagnosing of valvular heart disease , 2010, Expert Syst. Appl..
[204] Miguel Ángel Guevara-López,et al. An evaluation of image descriptors combined with clinical data for breast cancer diagnosis , 2013, International Journal of Computer Assisted Radiology and Surgery.
[205] Aboul Ella Hassanien,et al. Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks , 2012, J. Appl. Log..
[206] Hiba Chougrad,et al. Deep Convolutional Neural Networks for breast cancer screening , 2018, Comput. Methods Programs Biomed..
[207] Hehua Zhang,et al. Cross-task extreme learning machine for breast cancer image classification with deep convolutional features , 2020, Biomed. Signal Process. Control..
[208] Reyer Zwiggelaar,et al. Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks , 2018, IEEE Journal of Biomedical and Health Informatics.
[209] R. Barzilay,et al. External Validation of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice. , 2020, Academic radiology.
[210] Rajesh Mehra,et al. Breast cancer histology images classification: Training from scratch or transfer learning? , 2018, ICT Express.
[211] Jae Young Choi,et al. A generalized multiple classifier system for improving computer-aided classification of breast masses in mammography , 2015 .
[212] Seokmin Han,et al. A deep learning framework for supporting the classification of breast lesions in ultrasound images , 2017, Physics in medicine and biology.
[213] Aytug Onan,et al. A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer , 2015, Expert Syst. Appl..
[214] LinLin Shen,et al. Robust object representation by boosting-like deep learning architecture , 2016, Signal Process. Image Commun..
[215] Luiz Eduardo Soares de Oliveira,et al. Multiple instance learning for histopathological breast cancer image classification , 2019, Expert Syst. Appl..
[216] Sang-Woong Lee,et al. Classification of breast cancer histology images using incremental boosting convolution networks , 2019, Inf. Sci..
[217] C. M. Sujatha,et al. Asymmetry analysis of breast thermograms using BM3D technique and statistical texture features , 2014, 2014 International Conference on Informatics, Electronics & Vision (ICIEV).
[218] R. Chang,et al. The adaptive computer‐aided diagnosis system based on tumor sizes for the classification of breast tumors detected at screening ultrasound , 2017, Ultrasonics.
[219] R. A. Lerski,et al. Magnetic resonance imaging texture analysis classification of primary breast cancer , 2016, European Radiology.
[220] Václav Snásel,et al. Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[221] Hela Mahersia,et al. Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: A comparative analysis , 2016, Comput. Methods Programs Biomed..
[222] Qi Zhang,et al. Deep learning based classification of breast tumors with shear-wave elastography. , 2016, Ultrasonics.
[223] Murat Karabatak,et al. A new classifier for breast cancer detection based on Naïve Bayesian , 2015 .
[224] Yi Wang,et al. Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning. , 2020, Ultrasound in medicine & biology.