A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification

A computer-aided diagnosis (CAD) system requires detection, segmentation, and classification in one framework to assist radiologists efficiently in an accurate diagnosis. In this paper, a completely integrated CAD system is proposed to screen digital X-ray mammograms involving detection, segmentation, and classification of breast masses via deep learning methodologies. In this work, to detect breast mass from entire mammograms, You-Only-Look-Once (YOLO), a regional deep learning approach, is used. To segment the mass, full resolution convolutional network (FrCN), a new deep network model, is proposed and utilized. Finally, a deep convolutional neural network (CNN) is used to recognize the mass and classify it as either benign or malignant. To evaluate the proposed integrated CAD system in terms of the accuracies of detection, segmentation, and classification, the publicly available and annotated INbreast database was utilized. The evaluation results of the proposed CAD system via four-fold cross-validation tests show that a mass detection accuracy of 98.96%, Matthews correlation coefficient (MCC) of 97.62%, and F1-score of 99.24% are achieved with the INbreast dataset. Moreover, the mass segmentation results via FrCN produced an overall accuracy of 92.97%, MCC of 85.93%, and Dice (F1-score) of 92.69% and Jaccard similarity coefficient metrics of 86.37%, respectively. The detected and segmented masses were classified via CNN and achieved an overall accuracy of 95.64%, AUC of 94.78%, MCC of 89.91%, and F1-score of 96.84%, respectively. Our results demonstrate that the proposed CAD system, through all stages of detection, segmentation, and classification, outperforms the latest conventional deep learning methodologies. Our proposed CAD system could be used to assist radiologists in all stages of detection, segmentation, and classification of breast masses.

[1]  Md. Zia Uddin,et al.  A Single Depth Sensor Based Human Activity Recognition via Convolutional Neural Network , 2017 .

[2]  Clarimar José Coelho,et al.  Computer-aided diagnosis in chest radiography for detection of childhood pneumonia , 2008, Int. J. Medical Informatics.

[3]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[4]  Dagan Feng,et al.  Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks , 2017, IEEE Transactions on Biomedical Engineering.

[5]  Andrew P. Bradley,et al.  Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning , 2017, IEEE Transactions on Medical Imaging.

[6]  Gustavo Carneiro,et al.  Deep structured learning for mass segmentation from mammograms , 2014, 2015 IEEE International Conference on Image Processing (ICIP).

[7]  Behrouz Minaei,et al.  Assessment of a novel mass detection algorithm in mammograms. , 2013, Journal of cancer research and therapeutics.

[8]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  L.M. Bruce,et al.  Digital Mammographic Computer Aided Diagnosis (CAD) using Adaptive Level Set Segmentation , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Maryellen L. Giger.  Medical imaging and computers in the diagnosis of breast cancer , 2014, Optics & Photonics - Optical Engineering + Applications.

[11]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[12]  Asoke K. Nandi,et al.  Toward breast cancer diagnosis based on automated segmentation of masses in mammograms , 2009, Pattern Recognit..

[13]  C. Balleyguier,et al.  Computed-aided diagnosis (CAD) in the detection of breast cancer. , 2013, European journal of radiology.

[14]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[15]  Jaime S. Cardoso,et al.  Closed Shortest Path in the Original Coordinates with an Application to Breast Cancer , 2015, Int. J. Pattern Recognit. Artif. Intell..

[16]  Rafael Llobet,et al.  Computer-aided detection of prostate cancer , 2007, Int. J. Medical Informatics.

[17]  Hiroshi Fujita,et al.  Breast mass classification on mammograms using radial local ternary patterns , 2016, Comput. Biol. Medicine.

[18]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  M. A. Al-masni,et al.  Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  Berkman Sahiner,et al.  Computer-aided detection of breast masses on full field digital mammograms. , 2005, Medical physics.

[21]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[22]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[23]  Gustavo Carneiro,et al.  A deep learning approach for the analysis of masses in mammograms with minimal user intervention , 2017, Medical Image Anal..

[24]  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).

[25]  Yilong Yin,et al.  Breast masses in mammography classification with local contour features , 2017, Biomedical engineering online.

[26]  Shiju Yan,et al.  A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology. , 2017, Journal of X-ray science and technology.

[27]  François Chollet,et al.  Keras: The Python Deep Learning library , 2018 .

[28]  Jaime S. Cardoso,et al.  INbreast: toward a full-field digital mammographic database. , 2012, Academic radiology.

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

[30]  Gustavo Carneiro,et al.  Deep Learning and Structured Prediction for the Segmentation of Mass in Mammograms , 2015, MICCAI.

[31]  Yuting Zhang,et al.  Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Abhishek Midya,et al.  Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns , 2018, Expert Syst. Appl..

[33]  Xinbo Gao,et al.  A deep feature based framework for breast masses classification , 2016, Neurocomputing.

[34]  Yuya Kajikawa,et al.  Computer-aided diagnosis: A survey with bibliometric analysis , 2017, Int. J. Medical Informatics.

[35]  Chein-I Chang,et al.  A computer-aided design mammography screening system for detection and classification of microcalcifications , 2000, Int. J. Medical Informatics.

[36]  Cristian Ochoa,et al.  Positive psychological functioning in breast cancer: An integrative review. , 2016, Breast.

[37]  Age K. Smilde,et al.  Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies , 2011, Metabolomics.

[38]  Nico Karssemeijer,et al.  Large scale deep learning for computer aided detection of mammographic lesions , 2017, Medical Image Anal..

[39]  A. Jemal,et al.  Cancer statistics, 2017 , 2017, CA: a cancer journal for clinicians.

[40]  Manu Goyal,et al.  Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks , 2017, BIOINFORMATICS.

[41]  José García Rodríguez,et al.  A Review on Deep Learning Techniques Applied to Semantic Segmentation , 2017, ArXiv.

[42]  Yasser M. Kadah,et al.  An Automatic Computer-Aided Diagnosis System for Breast Cancer in Digital Mammograms via Deep Belief Network , 2018 .

[43]  Nico Karssemeijer,et al.  A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography , 2016, Digital Mammography / IWDM.

[44]  Hao Chen,et al.  Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks , 2017, IEEE Transactions on Medical Imaging.

[45]  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..

[46]  Cesare Furlanello,et al.  A Comparison of MCC and CEN Error Measures in Multi-Class Prediction , 2010, PloS one.

[47]  Ghassan Hamarneh,et al.  Mammography Segmentation with Maximum Likelihood Active Contours , 2022 .

[48]  Kenji Suzuki,et al.  A dual-stage method for lesion segmentation on digital mammograms. , 2007, Medical physics.

[49]  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..

[50]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[51]  Hong Liu,et al.  A novel segmentation method for convex lesions based on dynamic programming with local intra-class variance , 2012, SAC '12.

[52]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[53]  Xuelong Li,et al.  Mammographic mass segmentation: Embedding multiple features in vector-valued level set in ambiguous regions , 2011, Pattern Recognit..

[54]  Maryellen L. Giger,et al.  Automated seeded lesion segmentation on digital mammograms , 1998, IEEE Transactions on Medical Imaging.

[55]  A. Jemal,et al.  Cancer Statistics, 2008 , 2008, CA: a cancer journal for clinicians.

[56]  Miguel Ángel Guevara-López,et al.  Representation learning for mammography mass lesion classification with convolutional neural networks , 2016, Comput. Methods Programs Biomed..

[57]  Leonid Karlinsky,et al.  A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography , 2016, LABELS/DLMIA@MICCAI.

[58]  Yading Yuan,et al.  Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance , 2017, IEEE Transactions on Medical Imaging.

[59]  Nilanjan Dey,et al.  PCA-PNN and PCA-SVM Based CAD Systems for Breast Density Classification , 2016, Applications of Intelligent Optimization in Biology and Medicine.