Normal and Abnormal Masses Detection in Mammography Images Using Deep Convolutional Neural Network (DCNN)

Document Type : Computer Article

Authors

Assistant Professor, Department of Computer Engineering, National University of Skills (NUS), Tehran, Iran

Abstract

One of the most important and influential ways to diagnose breast cancer, especially in the early stages of the disease, is mammography. Mammography images are usually of low quality due to the complexity of breast tissues, the similarity between cancerous masses and normal tissues, the different sizes and shapes of the masses, and X-ray radiation. Therefore, it is very difficult to detect lesions, especially in the early stages; Because some mass lesions are embedded in natural tissues and have weak margins or vague margins. The proposed method in this study is to present an architecture based on a deep convolutional neural network to detect cancerous masses in mammography images, which ultimately leads to classifying the masses into normal and abnormal classes. The training of the proposed network begins with the modification of the images in the pre-processing stage in order to perform more accurate drawings with high resolution on the images and finally to improve the accuracy and sensitivity of separating the mass from the breast tissue for correct diagnosis. Python programming language and TensorFlow library have been used in the Windows environment to implement the proposed method. To ensure the performance of the proposed method, the cross-validation method was used and the obtained results were evaluated by the criteria of precision, accuracy, and sensitivity. The results obtained with an accuracy of 97.67% indicate the improvement of the diagnosis accuracy and the cost reduction in the diagnosis process

Keywords

Main Subjects

References

[1] E.I. Obeagu, and G.U. Obeagu. "Breastfeeding’s protective role in alleviating breast cancer burden: A comprehensive review." Annals of Medicine and Surgery 86, no. 5 (2024): 2805-2811.
[2] A. Dibden, J. Offman, S.W. Duffy, and R. Gabe. "Worldwide review and meta-analysis of cohort studies measuring the effect of mammography screening programmes on incidence-based breast cancer mortality." Cancers 12, no. 4 (2020): 976.
[3] K.P. Trayes, and S.E. Cokenakes. "Breast cancer treatment." American Family Physician 104, no. 2 (2021): 171-178.
[4] I. Sechopoulos, J. Teuwen, and R. Mann. "Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art." In Seminars in Cancer Biology, vol. 72, pp. 214-225. Academic Press, 2021.
[5] Z. Jafari, and E. Karami. "Breast cancer detection in mammography images: A CNN-based approach with feature selection." Information 14, no. 7 (2023): 410.
[6] G.O. Kuttan, and M.S. Elayidom. "Review on Computer Aided Breast Cancer Detection and Diagnosis using Machine Learning Methods on Mammogram Image." Current Medical Imaging 19, no. 12 (2023): 1361-1371.
[7] J.G. Melekoodappattu, A.S. Dhas, B.K. Kandathil, and K.S. Adarsh. "Breast cancer detection in mammogram: Combining modified CNN and texture feature based approach." Journal of Ambient Intelligence and Humanized Computing 14, no. 9 (2023): 11397-11406.
[8] V. Narayan, M. Faiz, P.K. Mall, and S. Srivastava. "A Comprehensive Review of Various Approach for Medical Image Segmentation and Disease Prediction." Wireless Personal Communications 132, no. 3 (2023): 1819-1848.
[9] F. Hoseini, A. Shahbahrami, and P. Bayat. "An efficient implementation of deep convolutional neural networks for MRI segmentation." Journal of Digital Imaging 31, no. 5 (2018): 738-747.
[10] F. Hoseini, A. Shahbahrami, and P. Bayat. "AdaptAhead optimization algorithm for learning deep CNN applied to MRI segmentation." Journal of Digital Imaging 32 (2019): 105-115.
[11] A. Kebaili, J. Lapuyade-Lahorgue, and S. Ruan. "Deep learning approaches for data augmentation in medical imaging: a review." Journal of Imaging 9, no. 4 (2023): 81.
[12] A. Raza, N. Ullah, J. Ali Khan, M. Assam, A. Guzzo, and H. Aljuaid. "DeepBreastCancerNet: A novel deep learning model for breast cancer detection using ultrasound images." Applied Sciences 13, no. 4 (2023): 2082.
[13] G.W. Lindsay. "Convolutional neural networks as a model of the visual system: Past, present, and future." Journal of Cognitive Neuroscience 33, no. 10 (2021): 2017-2031.
[14] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou. "A survey of convolutional neural networks: analysis, applications, and prospects." IEEE Transactions on Neural Networks and Learning Systems 33, no. 12 (2021): 6999-7019.
[15] D.X. Zhou. "Universality of deep convolutional neural networks." Applied and Computational Harmonic Analysis 48, no. 2 (2020): 787-794.
[16] A. Bouti, M.A. Mahraz, J. Riffi, and H. Tairi. "A robust system for road sign detection and classification using LeNet architecture based on convolutional neural network." Soft Computing 24, no. 9 (2020): 6721-6733.
[17] Y. Lee, and S. Nam. "Performance comparisons of AlexNet and GoogLeNet in cell growth inhibition IC50 prediction." International Journal of Molecular Sciences 22, no. 14 (2021): 7721.
[18] S.A. Hassan, M.S. Sayed, M.I. Abdalla, and M.A. Rashwan. "Breast cancer masses classification using deep convolutional neural networks and transfer learning." Multimedia Tools and Applications 79, no. 41 (2020): 30735-30768.
[19] V.H. Josephine, A.P. Nirmala, and V.L. Alluri. "Impact of hidden dense layers in convolutional neural network to enhance performance of classification model." In IOP Conference Series: Materials Science and Engineering, vol. 1131, no. 1, p. 012007. IOP Publishing, 2021.
[20] A. Dhillon, and G.K. Verma. "Convolutional neural network: a review of models, methodologies and applications to object detection." Progress in Artificial Intelligence 9, no. 2 (2020): 85-112.
[21] A. Jha, J.C. Peterson, and T.L. Griffiths. "Extracting low‐dimensional psychological representations from convolutional neural networks." Cognitive Science 47, no. 1 (2023): e13226.
[22] A. Zafar, M. Aamir, N.M. Nawi, A. Arshad, S. Riaz, A. Alruban, A.K. Dutta, and S. Almotairi. "A comparison of pooling methods for convolutional neural networks." Applied Sciences 12, no. 17 (2022): 8643.
[23] C. Ozdemir, Y. Dogan, and Y. Kaya. "A new local pooling approach for convolutional neural network: local binary pattern." Multimedia Tools and Applications 83, no. 12 (2024): 34137-34151.
[24] Kılıçarslan, Serhat, Kemal Adem, and Mete Çelik. "An overview of the activation functions used in deep learning algorithms." Journal of New Results in Science 10, no. 3 (2021): 75-88.
[25] M. Turkoglu, D. Hanbay, and A. Sengur. "Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests." Journal of Ambient Intelligence and Humanized Computing 13, no. 7 (2022): 3335-3345.
[26] H. Rahman, T.F.N. Bukht, R. Ahmad, A. Almadhor, and A.R. Javed. "Efficient breast cancer diagnosis from complex mammographic images using deep convolutional neural network." Computational Intelligence and Neuroscience 2023 (2023).
[27] H. Li, D. Chen, W.H. Nailon, M.E. Davies, and D.I. Laurenson. "Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography." IEEE Transactions on Medical Imaging 41, no. 1 (2021): 3-13.
[28] Y.D. Zhang, S.C. Satapathy, D.S. Guttery, J.M. Górriz, and S.H. Wang. "Improved breast cancer classification through combining graph convolutional network and convolutional neural network." Information Processing & Management 58, no. 2 (2021): 102439.
[29] S.A. Alanazi, M.M. Kamruzzaman, M.N.I. Sarker, M. Alruwaili, Y. Alhwaiti, N. Alshammari, and M.H. Siddiqi. "Boosting breast cancer detection using convolutional neural network." Journal of Healthcare Engineering 2021 (2021).
[30] F.F. Ting, Y.J. Tan, and K.S. Sim. "Convolutional neural network improvement for breast cancer classification." Expert Systems with Applications 120 (2019): 103-115.
[31] H. Chougrad, H. Zouaki, and O. Alheyane. "Deep convolutional neural networks for breast cancer screening." Computer Methods and Programs in Biomedicine 157 (2018): 19-30.
[32] A. Rakhlin, A. Shvets, V. Iglovikov, and A.A. Kalinin. "Deep convolutional neural networks for breast cancer histology image analysis." In Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018, Proceedings 15, pp. 737-744. Springer International Publishing, 2018.
[33] I.C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M.J. Cardoso, and J.S. Cardoso. "Inbreast: toward a full-field digital mammographic database." Academic Radiology 19, no. 2 (2012): 236-248.
[34] S. Asadzadeh, and B. Ravaei. "diagnosis of breast cancer at the molecular-cellular level with an artificial intelligence approach." Journal of Modeling in Engineering 21, no. 72 (2023): 19-30. (in Persian)
[35] A. Rashno, S. Fadaei, and A. Hamidi. "Automatic Speaker Recognition based on Gabor Features and Convolutional Neural Networks." Journal of Modeling in Engineering 21, no. 72 (2023): 49-67. (in Persian)
 
Journal of Modeling in Engineering
Volume 22, Issue 79
November 2024
Pages 29-43

Files

History

  • Receive Date: 29 June 2023
  • Revise Date: 13 May 2024
  • Accept Date: 18 May 2024

Share

How to cite

Statistics