A Novel Solution Based on Scale Invariant Feature Transform Descriptors and Deep Learning for the Detection of Suspicious Regions in Mammogram Images
DOI: 10.4103/jmss.JMSS_31_19
Abstract
Background: Deep learning methods have become popular for their high-performance rate in the classification and detection of events in computer vision tasks. Transfer learning paradigm is widely adopted to apply pretrained convolutional neural network (CNN) on medical domains overcoming the problem of the scarcity of public datasets. Some investigations to assess transfer learning knowledge inference abilities in the context of mammogram screening and possible combinations with unsupervised techniques are in progress. Methods: We propose a novel technique for the detection of suspicious regions in mammograms that consist of the combination of two approaches based on scale invariant feature transform (SIFT) keypoints and transfer learning with pretrained CNNs such as PyramidNet and AlexNet fine-tuned on digital mammograms generated by different mammography devices. Preprocessing, feature extraction, and selection steps characterize the SIFT-based method, while the deep learning network validates the candidate suspicious regions detected by the SIFT method. Results: The experiments conducted on both mini-MIAS dataset and our new public dataset Suspicious Region Detection on Mammogram from PP (SuReMaPP) of 384 digital mammograms exhibit high performances compared to several state-of-the-art methods. Our solution reaches 98% of sensitivity and 90% of specificity on SuReMaPP and 94% of sensitivity and 91% of specificity on mini-MIAS. Conclusions: The experimental sessions conducted so far prompt us to further investigate the powerfulness of transfer learning over different CNNs and possible combinations with unsupervised techniques. Transfer learning performances’ accuracy may decrease when the training and testing images come out from mammography devices with different properties.
Keywords
Full Text:
PDFReferences
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394-424.
Muramatsu C, Hara T, Endo T, Fujita H. Breast mass classification on mammograms using radial local ternary patterns. Comput Biol Med 2016;72:43-53.
Watanabe AT, Lim V, Vu HX, Chim R, Weise E, Liu J, et al. Improved cancer detection using artificial intelligence: A retrospective evaluation of missed cancers on mammography. J Digit Imaging 2019;32:625-37.
-Secretan B, Scoccianti C, Loomis D, Benbrahim-Tallaa L, Bouvard V, Bianchini F, et al. Breast-cancer screening–viewpoint of the IARC Working Group. N Engl J Med 2015;372:2353-8.
Dheeba J, Albert SN, Tamil SS. Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. J Biomed Inform 2014;49:45-52.
Surendiran B, Vadivel A. Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer. Int J Med Eng Inform 2012;4:36-54. Back to cited text no. 6
Kom G, Tiedeu A, Kom M. Automated detection of masses in mammograms by local adaptive thresholding. Comput Biol Med 2007;37:37-48.
Nishikawa RM, Giger ML, Doi K, Vyborny CJ, Schmidt RA. Computer-aided detection of clustered microcalcifications on digital mammograms. Med Biol Eng Comput 1995;33:174-8.
Hela B, Hela M, Kamel H, Sana B, Najla M. Breast Cancer Detection: A Review on Mammograms Analysis Techniques. 10th International Multi-Conference on Systems, Signals and Devices (SSD); 2013. p. 1-6.
Ganesan K, Acharya UR, Chua CK, Min LC, Abraham KT, Ng KH. Computer-aided breast cancer detection using mammograms: A review. IEEE Rev Biomed Eng 2013;6:77-98.
Li Y, Chen H, Cao L, Ma J. A survey of computer-aided detection of breast cancer with mammography. J Health Med Inf 2016;7:4. Back to cited text no. 11
Mustra M, Grgic M, Rangayyan RM. Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms. Med Biol Eng Comput 2016;54:1003-24.
Ardizzone E, Bruno A, Mazzola G. Scale detection via keypoint density maps in regular or near-regular textures. Pattern Recognit Lett 2013;34:2071-8.
Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vision 2004;60:91-110.
Bay H, Ess A, Tuytelaars T, Luc VG. Speeded-up robust features (SURF). Comput Vis Image Underst 2008;110:346-59.
Ali Y, Hamed S. Early breast cancer detection using mammogram images: A review of image processing techniques. Biosci Biotech Res Asia 2015;12:225-34.
Min D, Xiangyu L, Yide M, Yanan G, Yurun M, Wang K. An efficient approach for automated mass segmentation and classification in mammograms. J Digital Imaging 2015;28:613-25.
Kook KJ, Mi PJ, Sik SK, Wook PH. Detection of clustered microcalcifications on mammograms using surrounding region dependence method and artificial neural network. J VLSI Signal Proce 1998;18:251-62.
Rangaraj MR, Fabio JA. Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Med Biol Eng Comput 2006;44:883-94.
Anitha J, Dinesh PJ. Mammogram segmentation using maximal cell strength updation in cellular automata. Med Biol Eng Comput 2015;53:737-49.
Tingting M, Asoke KN, Rangaraj MR. Classification of breast masses via nonlinear transformation of features based on a kernel matrix. Med Biol Eng Comput 2007;45:769-80.
Vipul S, Sukhwinder S. CFS--SMO based classification of breast density using multiple texture models. Med Biol Eng Comput 2014;52:521-9.
Qiu G, Jianhua Z, Shengyong C, Andrew TP. Automatic segmentation of micro-calcification based on sift in mammograms. Int Conf Biomed Eng Inform 2008;2:13-7.
Aize C, Qing S, Xulei Y. Robust information clustering incorporating spatial information for breast mass detection in digitized mammograms. Comput Vis Image Underst 2008;109:86-96.
Kai H, Xieping G, Fei L. Detection of suspicious lesions by adaptive thresholding based on multiresolution analysis in mammograms. IEEE Trans Instrum Meas 2011;60:462-72.
Pereira DC, Ramos RP, do Nascimento MZ. Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Programs Biomed 2014;114:88-101.
de Sampaio WB, Diniz EM, Silva AC, de Paiva AC, Gattass M. Detection of masses in mammogram images using CNN, geostatistic functions and SVM. Comput Biol Med 2011;41:653-64.
Vikhe PS, Thool VR. Mass detection in mammographic images using wavelet processing and adaptive threshold technique. J Med Syst 2016;40:82.
Anitha J, Dinesh PJ, Pandian S, Alex I. A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms. Comput Methods Programs Biomed 2017;138:93-104.
Litjens G, Kooi T, Bejnordi BE, Setio AA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Analysis 2017;42:60-88.
Berkman S, Heang-Ping C, Petrick N, Datong W, Helvie MA, Adler DD, et al. Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images. IEEE Trans Med Imaging 1996;15:598-610.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436-44.
Pengcheng X, Chang S, Rafik G. Abnormality detection in mammography using deep convolutional neural networks. IEEE Int Symp Med Meas Appl 2018:1-6.
Daniel L, Arzav J. Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks. arXiv preprint arXiv: 1612.00542; 2016.
Tsochatzidis L, Zagoris K, Arikidis N, Karahaliou A, Costaridou L, Pratikakis I. Computer-aided diagnosis of mammographic masses based on a supervised content-based image retrieval approach. Pattern Recognit 2017;71:106-17.
Jung H, Kim B, Lee I, Yoo M, Lee J, Ham S, et al. Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS One 2018;13:e0203355. Back to cited text no. 36
Cai H, Huang Q, Rong W, Song Y, Li J, Wang J, et al. Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms. Comput Math Methods Med 2019;2019:2717454.
Richa A, Oliver D, Xavier L, Hoon YM, Robert M. Automatic mass detection in mammograms using deep convolutional neural networks. J Med Imaging 2019;6:31409.
Thijs K, Geert L, Bram VG, Albert GM, Clara IS, Ritse M, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Analy 2017;35:303-12. Back to cited text no. 39
Arfan JM. Deep learning based computer aided diagnosis system for breast mammograms. Int J Adv Comput Sci Appl 2017;8:286-90.
Wang J, Yang X, Cai H, Tan W, Jin C, Li L. Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci Rep 2016;6:27327.
Michiel K, Kersten P, Mads N, Andrew YN, Pengfei D, Christian I, et al. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 2016;35:1322-31.
Yamashita R, Nishio M, Do RK, Togashi K. Convolutional Neural Networks: An Overview and Application in Radiology. Insights into Imaging; 2018. p. 1-19.
Ribli D, Horváth A, Unger Z, Pollner P, Csabai I. Detecting and classifying lesions in mammograms with Deep Learning. Sci Rep 2018;8:4165.
Tavakoli N, Karimi M, Norouzi A, Karimi N, Samavi S, Soroushmehr SM Reza. Detection of abnormalities in mammograms using deep features. J Ambient Intell Humaniz Comput 2019. doi: 10.1007/s12652-019-01639-x.
Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, et al. The mammographic image analysis society digital mammogram database, exerpta medica. Int Congress Series 1994;1069:375-8.
Neeraj D, Gustavo C, Andrew BP, Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests. 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA); 2015. p. 1-8.
Bowyer K, Kopans D, Kegelmeyer WP, Moore R, Sallam M, Chang K, et al. The digital database for screening mammography, Third international workshop on digital mammography. 1996:58. p. 27.
Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. INbreast: Toward a full-field digital mammographic database. Acad Radiol 2012;19:236-48.
Akila AS, Anitha J, Pandian SI, Peter JD. Classification of mammogram images using multiscale all convolutional neural network (MA-CNN). J Med Syst 2020;44:30.
Dhungel N, Carneiro G, Bradley AP. The automated learning of deep features for breast mass classification from mammograms. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2016. p. 106-14.
Arevalo J, González FA, Ramos-Pollán R, Oliveira JL, Guevara Lopez MA. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed 2016;127:248-57.
BDCR. Available from: https://bcdr.eu/information/about. [Last accessed on 2020 May 25].
Teare P, Fishman M, Benzaquen O, Toledano E, Elnekave E. Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J Digital Imaging 2017;30:499-505.
Benjamin HQ, Hui L, Maryellen ML. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging 2016;3:1-5.
Levy D, Jain A. Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks, CoRR, vol. abs/1612.00542; 2016. Available from: http://arxiv.org/abs/1612.00542, arXiv. [Last accessed on 2016 Dec 02].
Agarwal R, Diaz O, Llad'o X, Yap MH, Marti R. Automatic mass detection in mammograms using deep convolutional neural networks. J Med Imaging 2019;6:31409.
SuReMaPP Dataset: Suspicious Regions on Mammograms Dataset from PP; 2019. Available from: https://mega.nz/#F!Ly5g0agB!-QL9uBEvoP8rNig8JBuYfw. [Last accessed on 2019 Jun 18].
Wang J, Perez L. The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis Recognit 2017;1-8. Available from: https://arxiv.org/pdf/1712.04621.pdf. [Last accessed on 2017 Dec 13].
Rahmat B, Joelianto E, Purnama I, Purnomo MH. An improved mean shift using adaptive fuzzy Gaussian kernel for Indonesia vehicle license plate tracking. IAENG Int J Comput Sci 2018;45:458-471.
Vedaldi A, Fulkerson B. VLFeat: An open and portable library of computer vision algorithms. Proceedings of the 18th ACM International Conference on Multimedia; 2010. p. 1469-72.
VLFeat Library SIFT Tutorial. Available from: http://www.vlfeat.org/overview/sift.html. [Last accessed on 2018 Jan 08].
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016;35:1285-98.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems; 2012. p. 1097-105. Back to cited text no. 64
Dongyoon H, Jiwhan K, Junmo K, Deep Pyramidal Residual Networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017. p. 5927-35.
Deng J, Dong W, Socher R, Li-Jia L, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition CVPR; 2009. p. 248-55.
Zhang T. Solving Large Scale Linear Prediction Problems Using Stochastic Gradient Descent Algorithms. Proceedings of the Twenty- first International Conference on Machine Learning ICML; 2004. p. 116.
Ioffee S, Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, arXiv preprint arXiv: 1502.03167; 2015.
Zhang C, Bengio S, Hardt M, Recht B, Vinyals O. Understanding Deep Learning Requires Rethinking Generalization. arXiv preprint arXiv: 1611.03530; 2016.
LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE (1998) Vol 86 pag. 2278-324.
Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 2014;15:1929-58.
Burçin K, Nabiyev VV, Turhan K. A novel automatic suspicious mass regions identification uasing Havrda & Charvat entropy and Otsu's N thresholding. Comput Methods Programs Biomed 2014;114:349-60.
He K, Zhang X, Ren S, Sun J. Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification. In: ICCV; 2015.
Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, et al. Caffe: Convolutional Architecture for Fast Feature Embedding. arXiv preprint arXiv: 1408.5093; 2014.
Refbacks
- There are currently no refbacks.
https://e-rasaneh.ir/Certificate/22728
ISSN : 2228-7477