MLO and CC view of feature fusion and mammogram classification using deep convolution neural network

V. Sridevi; J. Abdul Samath

doi:10.53730/ijhs.v6nS7.13106

Authors

V. Sridevi Assistant Professor, Department of Computer Science, PSG College of Arts & Science and Research Scholar of Govt. Arts College, Udumalpet, India
J. Abdul Samath Assistant Professor, Department of Computer Science, Chikkanna Government Arts College, Tirupur, India

Keywords:

Mammogram, Breast Cancer, CNN, Feature Extraction, Feature Fusion, Convolution, Pooling, MLO and CC Views, Classification

Abstract

Breast cancer is the most frequent type of cancer in women all over the world. The improvement of computer aided system help the radiologist for the effective analysis and diagnosis of breast cancer. It presents a computational methodology for classifying breast cancer as normal, benign and malignant from CC and MLO views of mammogram image. The proposed strategy consists of feature extraction, multiple view feature fusion and classification. The input images are fed into feature extraction where convolution neural network is applied. The CNN is nicely suitable for both feature extraction, feature fusion and mammogram classification. In this framework, convolution layer, pooling and activation function are used as a feature extraction techniques. After the process of feature extraction, feature fusion is employed by average pooling of CNN. The feature fusion will increase or maximize the relevant information of the breast image. Finally obtained features from the fusion are fed into CNN classifier in which softmax and fully connected layer are employed as a classifier techniques. The proposed work achieves 98.4% of accuracy to classify the breast cancer from MLO and CC views using hybrid feature with CNN classifier.

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References

J. Ferlay , I. Soerjomataram , R. Dikshit , S. Eser , C. Mathers , M. Rebelo , D.M. Parkin , D. Forman , F. Bray , Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012, Int. J. Cancer 136 (5)(2015) E359–E386 .

G.B. Junior , S.V. da Rocha , M. Gattass , A.C. Silva , A.C. de Paiva , A mass classification using spatial diversity approaches in mammography images for false positive reduction, Expert Syst. Appl. 40 (18) (2013) 7534–7543 .

W.B. Sampaio , E.M. Diniz , A.C. Silva , A.C. De Paiva , M. Gattass , Detection of masses in mammogram images using cnn, geostatistic functions and svm, Comput. Biol. Med. 41 (8) (2011) 653–664 .

Mohamed A. Berbar, “Hybrid methods for feature extraction for breast masses classification”, Egyptian Informatics Journal 19 (2018) 63–73

Shankar Thawkar, Ranjana Ingolikar, “Classification of masses in digital mammograms using Biogeography-based optimization technique”, Journal of King Saud University – Computer and Information Sciences (2018),

D.R. Ericeira , A.C. Silva , A.C. De Paiva , M. Gattass , Detection of masses based on asymmetric regions of digital bilateral mammograms using spatial description with variogram and cross-variogram functions, Comput. Biol. Med. 43 (8) (2013) 987–999.

Prabhpreet Kaur, Gurvinder Singh, Parminder Kaur, “Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification”, Informatics in Medicine Unlocked 16 (2019).

W.B. de Sampaio , A.C. Silva , A.C. de Paiva , M. Gattass , Detection of masses in mammograms with adaption to breast density using genetic algorithm, phylo- genetic trees, lbp and svm, Expert Syst. Appl. 42 (22) (2015) 8911–8928

Mohamed Abdel-Nasser, Antonio Moreno, Domenec Puig, “Temporal mammogram image registration using optimized curvilinear coordinates”, computer methods and programs in biomedicine 127(2016) 1-14.

Raghavendra, U., Rajendra Acharya, U., Fujita, H., Gudigar, A., Tan, J.H., Chokkadi, S., 2016. Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images. Appl. Soft Comput. J. 46, 151–161.

M. Malignant and benign mass segmentation in mammograms using active contour methods. Symmetry 2017; 9(277):1–22.

Khehra, B., Pharwaha, A.,. Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications. J. Inst. Eng. (India): Ser. B 98 (2), 2017189– 202.

R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973.

S. Duraisamy, S. EmperumalComputer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifierDeep Learning in Computer Vision, 11 (8) (2017), pp. 656-662,

A. Rakhlin, A. Shvets, V. Iglovikov, A.A. KalininDeep convolutional neural networks for breast cancer histology image analysis A. Campilho, F. Karray, B. ter Haar Romeny (Eds.), Image analysis and recognition. ICIAR 2018. Lecture notes in computer science, vol. 10882, Springer, Chamdoi (2018

L. Johnson, D. Ritter, Observation of periodic waves in a pulse-coupled neural network, Optical Letter 18 (1993) 1253.

H.S. Ranganath, G. Kuntimad, J.L. Johnson, Pulse coupled neural networks for image processing, in: Proc. of the Southeast Conference on ‘Visualize the future’, 1995, pp. 37–43.

Zhaobin Wang, Yide Ma, Feiyan Cheng, Lizhen Yang, “Review of pulse-coupled neural networks”, Image and Vision Computing 28 (2010) 5–13.

Keiron O'Shea, Ryan Nash, “An Introduction to Convolutional Neural Networks”, 2015.

Albawi, Saad & Abed Mohammed, Tareq & ALZAWI, Saad. (2017). Understanding of a Convolutional Neural Network. 10.1109/ICEngTechnol.2017.8308186.

Jianxin Wu, “Convolutional neural networks”,LAMDA Group, National Key Lab for Novel Software Technology, Nanjing University, February 11, 2020.

Dalalyan AS, Hebiri M, Lederer J. On the prediction performance of the lasso. Bernoulli 2017;23:552–81.

A. Jouirou, A. Baâzaoui, and W. Barhoumi, ``Multi-view information fusion in mammograms: A comprehensive overview,'' Inf. Fusion, vol. 52, pp. 308_321, Dec. 2019.

V. Sridevi, J. Abdul Samath, “A Survey on Breast Cancer Segmentation and Classification Using Several Methods” in International Journal of Scientific Research in Computer Science Applications and Management Studies, Volume 8, Issue 3 (May 2019)

Gustavo Carneiro, Jacinto Nascimento, and Andrew P Bradley, “Unregistered multiview mammogram analysis with pre-trained deep learning models,” in MICCAI. Springer, 2015, pp. 652–660.

Neeraj Dhungel, Gustavo Carneiro, and Andrew P Bradley, “Deep learning and structured prediction for the segmentation of mass in mammograms,” in MICCAI. Springer, 2015, pp. 605–612.

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, “Imagenet classification with deep convolutional neural networks.,” in NIPS, 2012, vol. 1, p. 4.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, “Deep residual learning for image recognition,” arXiv preprint arXiv:1512.03385, 2015.

Neeraj Dhungel Gustavo Carneiro, and Andrew P Bradley, “Automated mass detection in mammograms using cascaded deep learning and random forests,” in DICTA. IEEE, 2015, pp. 1–8.

V. Sridevi, J.Abdul Samath, Advancement on Breast Cancer Detection Using Medio-Lateral-Oblique (Mlo) and Cranio-Caudal (CC) Features”, Volume 83, May - June 2020, pp. 85-93.

Christian Szegedy and et al., “Going deeper with convolutions,” in ICCV, 2015, pp. 1–9.