A deep convolutional neural network architecture for breast mass classification using mammogram images
Abstract
Breast cancer is one of the second most common cancer occurring worldwide. Early identification of the disease is a major interest that promises to propose several diagnostic procedures to prevent further surgical interventions. This research paper aims to develop a breast mass classifier system using deep learning to differentiate breast mass images from normal mammographic images. The benchmark mammographic datasets CBIS-DDSM, INbreast, and mini-MIAS are used for constructing the proposed model DELU-BM-CNN. The region of interest is identified by applying image processing techniques (median filter, binarization and dilation) and the images are enhanced and sharpened using adaptive histogram equalization and unsharp masking techniques. The pre-processed images are trained with a minimum of five deep convolutional layers activated by an Exponential Linear Unit (ELU) which is developed from scratch for feature learning and classifying the given whole mammographic images. Dropout, Data normalization, and Global average pooling are some of the regularization techniques adopted to prevent the model from over-fitting. The proposed models are able to classify CBIS-DDSM images with an accuracy of 96.60%, INbreast images with 96.20% and MIAS images with 97.40%. The experimental results are also compared with conventional Rectified Linear Unit (ReLU) and Leaky ReLU activation function that promises the proposed model as a good prognosticator than the state-of-art models for cancer diagnosis using mammogram images as input.
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1. Arnold M, Morgan E, Rumgay H, et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. The Breast. 2022, 66: 15-23. doi: 10.1016/j.breast.2022.08.010
2. Tariq M, Iqbal S, Ayesha H, et al. Medical image based breast cancer diagnosis: State of the art and future directions. Expert Systems with Applications. 2021, 167: 114095. doi: 10.1016/j.eswa.2020.114095
3. Tsochatzidis L, Costaridou L, Pratikakis I. Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study. Journal of Imaging. 2019, 5(3): 37. doi: 10.3390/jimaging5030037
4. Iranmakani S, Mortezazadeh T, Sajadian F, et al. A review of various modalities in breast imaging: technical aspects and clinical outcomes. Egyptian Journal of Radiology and Nuclear Medicine. 2020, 51(1). doi: 10.1186/s43055-020-00175-5
5. Harvey H, Karpati E, Khara G, et al. The Role of Deep Learning in Breast Screening. Current Breast Cancer Reports. 2019, 11(1): 17-22. doi: 10.1007/s12609-019-0301-7
6. Abdelhafiz D, Yang C, Ammar R, et al. Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinformatics. 2019, 20(S11). doi: 10.1186/s12859-019-2823-4
7. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017, 60(6): 84-90. doi: 10.1145/3065386
8. LeCun Y, Bengio Y. The Handbook of Brain Theory and Neural Networks. MIT Press; 1998.
9. Girshick R, Donahue J, Darrell T, et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition. 2014. doi: 10.1109/cvpr.2014.81
10. Zhang Y, Sohn K, Villegas R, et al. Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015. doi: 10.1109/cvpr.2015.7298621
11. Lu L, Zheng Y, Carneiro G, et al. Deep Learning and Convolutional Neural Networks for Medical Image Computing. Springer International Publishing. 2017. doi: 10.1007/978-3-319-42999-1
12. Dong S, Wang P, Abbas K. A survey on deep learning and its applications. Computer Science Review. 2021, 40: 100379. doi: 10.1016/j.cosrev.2021.100379
13. Shen L, Margolies LR, Rothstein JH, et al. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Scientific Reports. 2019, 9(1). doi: 10.1038/s41598-019-48995-4
14. Zhang Q, Li Y, Zhao G, et al. A Novel Algorithm for Breast Mass Classification in Digital Mammography Based on Feature Fusion. Journal of Healthcare Engineering. 2020, 2020: 1-11. doi: 10.1155/2020/8860011
15. Malebary SJ, Hashmi A. Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram. IEEE Access. 2021, 9: 55312-55328. doi: 10.1109/access.2021.3071297
16. Busaleh M, Hussain M, Aboalsamh HA, et al. Breast Mass Classification Using Diverse Contextual Information and Convolutional Neural Network. Biosensors. 2021, 11(11): 419. doi: 10.3390/bios11110419
17. Sun L, Wen J, Wang J, et al. Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography. IET Biometrics. 2022, 11(6): 588-600. doi: 10.1049/bme2.12076
18. Sannasi Chakravarthy SR, Rajaguru H. Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning. IRBM. 2022, 43(1): 49-61. doi: 10.1016/j.irbm.2020.12.004
19. Mahmood T, Li J, Pei Y, et al. Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach. PLOS ONE. 2022, 17(1): e0263126. doi: 10.1371/journal.pone.0263126
20. Belhaj Soulami K, Kaabouch N, Nabil Saidi M. Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network. Biomedical Signal Processing and Control. 2022, 76: 103696. doi: 10.1016/j.bspc.2022.103696
21. Sengan S, Priya V, Syed Musthafa A, et al. A fuzzy based high-resolution multi-view deep CNN for breast cancer diagnosis through SVM classifier on visual analysis. Journal of Intelligent & Fuzzy Systems. 2020, 39(6): 8573-8586. doi: 10.3233/jifs-189174
22. Baccouche A, Garcia-Zapirain B, Elmaghraby AS. An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks. Scientific Reports. 2022, 12(1). doi: 10.1038/s41598-022-15632-6
23. Jiang J, Peng J, Hu C, et al. Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm. Artificial Intelligence in Medicine. 2022, 134: 102419. doi: 10.1016/j.artmed.2022.102419
24. Baccouche A, Garcia-Zapirain B, Zheng Y, et al. Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques. Computer Methods and Programs in Biomedicine. 2022, 221: 106884. doi: 10.1016/j.cmpb.2022.106884
25. Rajesh Kumar N, Bala Krishnan R, Manikandan G, et al. Reversible data hiding scheme using deep learning and visual cryptography for medical image communication. Journal of Electronic Imaging. 2022, 31(06). doi: 10.1117/1.jei.31.6.063028
26. Adam R, Dell’Aquila K, Hodges L, et al. Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review. Breast Cancer Research. 2023, 25(1). doi: 10.1186/s13058-023-01687-4
27. Balkenende L, Teuwen J, Mann RM. Application of Deep Learning in Breast Cancer Imaging. Seminars in Nuclear Medicine. 2022, 52(5): 584-596. doi: 10.1053/j.semnuclmed.2022.02.003
28. Civilibal S, Cevik KK, Bozkurt A. A deep learning approach for automatic detection, segmentation and classification of breast lesions from thermal images. Expert Systems with Applications. 2023, 212: 118774. doi: 10.1016/j.eswa.2022.118774
29. Wakili MA, Shehu HA, Sharif MdH, et al. Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning. Computational Intelligence and Neuroscience. 2022, 2022: 1-31. doi: 10.1155/2022/8904768
30. Ahmed M, Islam MdR. A combined feature-vector based multiple instance learning convolutional neural network in breast cancer classification from histopathological images. Biomedical Signal Processing and Control. 2023, 84: 104775. doi: 10.1016/j.bspc.2023.104775
31. Sirjani N, Ghelich Oghli M, Kazem Tarzamni M, et al. A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation. Physica Medica. 2023, 107: 102560. doi: 10.1016/j.ejmp.2023.102560
32. Rahman MM, Khan MSI, Babu HMdH. BreastMultiNet: A multi-scale feature fusion method using deep neural network to detect breast cancer. Array. 2022, 16: 100256. doi: 10.1016/j.array.2022.100256
33. Çayır S, Solmaz G, Kusetogullari H, et al. MITNET: a novel dataset and a two-stage deep learning approach for mitosis recognition in whole slide images of breast cancer tissue. Neural Computing and Applications. 2022, 34(20): 17837-17851. doi: 10.1007/s00521-022-07441-9
34. Kadry S, Crespo RG, Herrera-Viedma E, et al. Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes. Procedia Computer Science. 2023, 218: 24-34. doi: 10.1016/j.procs.2022.12.398
35. Lee RS, Gimenez F, Hoogi A, et al. A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific Data. 2017, 4(1). doi: 10.1038/sdata.2017.177
36. Moreira IC, Amaral I, Domingues I. INbreast. Academic Radiology. 2012, 19(2): 236-248. doi: 10.1016/j.acra.2011.09.014
37. Suckling J, Parker J, Dance DR, et al. Mammographic Image Analysis Society (MIAS). 1994.. 375–378.
38. Patterson J, Gibson A. Deep Learning. O’Reilly Media, Inc; 2017.
39. Zaccone G, Karim MR. Deep Learning with TensorFlow: Explore Neural Networks and Build Intelligent Systems with Python. Packt Publishing Limited; 2018.
40. Graupe D. Deep Learning Neural Networks: Design and Case Studies. World Scientific; 2020.
41. Wang SH, Satapathy SC, Xie MX, et al. ELUCNN for explainable COVID-19 diagnosis. Soft Computing. 2023. doi: 10.1007/s00500-023-07813-w
42. Sergey I, Christian S. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Available online: https://proceedings.mlr.press/v37/ioffe15.html (accessed on 20 July 2023).
43. Scherer D, Müller A, Behnke S. Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. Lecture Notes in Computer Science. 2010, 92-101. doi: 10.1007/978-3-642-15825-4_10
44. Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. 2014; 15: 1929–1958.
45. Lin M, Chen Q, Yan S. Network in Network. Presented at the International Conference on Learning Representations. 2014.
DOI: https://doi.org/10.32629/jai.v7i3.1288
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