International Journal of Nonlinear Analysis and Applications
A new parallel deep learning algorithm for breast cancer classification
Document Type : Research Paper
Authors
1 Department of Computer Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran
2 Computer Science Department, Amirkabir University of Technology, Tehran, Iran
3 Department of Mathematics, Izeh Branch,Islamic Azad University, Izeh,Iran
Abstract
Now diagnostic methods with the help of machine learning have been able to help doctors in this field. One of the most important of these methods is deep learning, which has gotten good answers in images containing cancer. Increasing the accuracy of deep neural network classifiers can increase the diagnosis of breast cancer. In this paper, we have tried to achieve higher accuracy than non-parallel models with the help of a parallel model of a deep neural network. The proposed method is a parallel hybrid method combining AlexNet and VGGNet networks applied in parallel to mammographic images. The database used in this article is INBreast. The results obtained from this method show a 4% increase compared to some other classification models so that in the type of density 1, it has achieved about 99.7%. In the case of other densities, an accuracy of nearly 99% has been obtained.
Keywords
[1] R. A. Ali, A. M. Mehdi and R. Rothnagel, A Rapid 3D z-crossings algorithm to segment electron tomograms and
extract organelles and macromolecules, J. Struct. Biol., 200 (2) (2017) 73-86.
[2] Sh. Amemiya, H. Takao and Sh. Kato, Automatic detection of brain metastases on contrast-enhanced CT with
deep-learning feature-fused single-shot detectors, Eur. J. Radiol., 136 (2021) 109577.
[3] A. Arjmand, S. Meshgini and R. Afrouzian, Breast Tumor Detection using Convolutional Neural Network in MRI
Images, J. Adv. Signal Process., 3 (2) (2020) 109-117.
[4] S. Bazrafkan and P. Corcoran, Semi-parallel deep neural networks (SPDNN), convergence and generalization,
arXiv preprint arXiv:1711.01963 (2017).
[5] M. Cadoni , A. Lagorio and E. Grosso, Incremental models based on features persistence for object recognition,Pattern Recognit. Lett. , 122 (2019) 38-44.
[6] C. Caradu, B. Spampinato and A.Maria, Fully automatic volume segmentation of infrarenal abdominal aortic
aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation, J. Vasc. Surg. , 74(1) (2021) 246-256.
[7] G. Carneiro , J. Nascimento and A.P. Bradley, Unregistered multiview mammogram analysis with pre-trained deep
learning models, Int. Conf. Med. Image Comput. Comput. Assisted Intervention , Springer, Cham, 2015.
[8] E. Castro, J.S. Cardoso and J.C. Pereira, Elastic deformations for data augmentation in breast cancer mass
detection, 2018 IEEE EMBS Int. Conf. Biomed. Health. Inf. (BHI). IEEE, 2018.
[9] J. Dolz, Ch. Desrosiers, L. Wang, J.Yuan, D. Shen and I.Ayed, Deep CNN ensembles and suggestive annotations
for infant brain MRI segmentation, Comput. Med. Imaging Graphics, 79 (2020) 101660.
[10] S. Ebrahimkhani and M. Hisham Jaward, A review on segmentation of knee articular cartilage: from conventional
methods towards deep learning, Artif. Intell. Med., 106 (2020) 101851.
[11] M.M. Eltoukhy and I. Faye, An adaptive threshold method for mass detection in mammographic images, 2013
IEEE Inter. Conf. Signal and Image Processing Appl.. IEEE, 2013.
[12] V. Fischer, J. K¨ohler and T. Pfeil, The streaming rollout of deep networks-towards fully model-parallel execution,
arXiv preprint arXiv:1806.04965 (2018).
[13] S.V. Fotin, Y. Yin, H. Haldankar, J.W. Hoffmeister and S. Periaswamy, Detection of soft tissue densities from
digital breast tomosynthesis: comparison of conventional and deep learning approaches, Med. Imaging 2016:
Computer-Aided Diagnosis. Vol. 9785. International Society for Optics and Photonics, 2016.
[14] M.H. Gholizadeh , H. Ghayoumi Zadeh, H. Fatehi Marj and N. Ahmadi Nejad, The Investigation of Deep
Convolutional Neural Network for Diagnosing Breast Cancer in Thermographic Images, Jundishapur Sci. Med.
J., 18 (6) (2020) 615-629.
[15] G. Hamed, M.A.E.R. Marey, S.E.S. Amin and M.F. Tolba, Deep learning in breast cancer detection and classification, Int. Conf. Artif. Intell. Comput. Vision, Springer, Cham, 2020.
[16] A. Hassan, F. Liu, F. Wang and Y. Wang, Secure content based image retrieval for mobile users with deep neural
networks in the cloud,J. Syst. Archit. , 116 (2021) 102043.
[17] B. W. Hong and B.S. Sohn, Segmentation of regions of interest in mammograms in a topographic approach, IEEE
Trans. Inf. Technol. Biomedicine, 14(1) (2009) 129-139.
[18] A. Karsaz and S. Mohammadian Roshan, Medical image processing using deep convolutional neural networks,
Electr. Asre Mag., 5 (11) (2019) 23-28.
[19] T. Liang and Edward Y. Lee, Interstitial Lung Diseases in Children, Adolescents, and Young Adults: Different
from Infants and Older Adults, Radiologic Clin. , 58( 3) (2020) 487-502.
[20] W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu and F.E. Alsaadi, A survey of deep neural network architectures and
their applications, Neurocomputing, 234 (2017) 11-26.
[21] S. Liuhanen, M. Sallisalmi, V. Pettil¨a, N. Oksala and J. Tenhunen, Indirect measurement of the vascular endothelial glycocalyx layer thickness in human submucosal capillaries with a plug-in for ImageJ, Comput. Methods
Programs Biomed. , 110(1) (2013) 38-47.
[22] T. Masuda, T. Nakaura, Y. Funama, K. Sugino, T. Sato, T. Yoshiura, Y. Baba and K. Awai, Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies,
Radiography (2021).
[23] D. Mishkin, N. Sergievskiy and J. Matas, Systematic evaluation of convolution neural network advances on the
imagenet,Comput. Vision Image Understanding , 161 (2017) 11-19.
[24] Y. Mori, Sh. Kudo and J. East, Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an
add-on analysis of a clinical trial (with video), Gastrointestinal endoscopy, 92 (4) (2020) 905-911.
[25] B. Mughal, M. Sharif, N. Muhammad and T. Saba, A novel classification scheme to decline the mortality rate
among women due to breast tumor, Microsc. Res. Tech. , 81(2) (2018) 171-180.[26] H. Pratt, F. Coenen, D.M. Broadbent, S.P. Harding and Y. Zheng, Convolutional neural networks for diabetic
retinopathy, Inter. Conf. Med. Imaging Understanding Anal., July 2016.
[27] Y. M. S. Reddy, R.E. Ravindran and K.H. Kishore, Diabetic retinopathy through retinal image analysis: A review,
Inter. J. Eng. Technol., 7(1.5) (2018) 19-25.
[28] M. Salvi, U. Rajendra Acharya, Filippo Molinari and Kristen M. Meiburger, The impact of pre-and post-image
processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis,
Comput. Biol. Med., (2020) 104129.
[29] N. Sharma, V. Jain and A. Mishra, An analysis of convolutional neural networks for image classification,Procedia
Comput. Sci., 132 (2018) 377-384.
[30] S. Suzuki, X. Zhang, N. Homma, K. Ichiji, N. Sugita, Y. Kawasumi, T. Ishibashi and M. Yoshizawa, Mass
detection using deep convolutional neural network for mammographic computer-aided diagnosis, 2016 55th Annu.
Conf. Soc. Instrum. Control Eng. Japan (SICE). IEEE, 2016.
[31] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich,
Going deeper with convolutions, Proc. IEEE conf. comput. vision and pattern recognition 2015, pp. 1-9.
[32] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, Rethinking the inception architecture for computer
vision , Proc. IEEE conf. comput. vision and pattern recognition. 2016.
[33] E. Takaya, Y. Takeichi, M. Ozaki and S. Kurihara, Sequential semi-supervised segmentation for serial electron
microscopy image with small number of labels, J. Neurosci. Methods , 351 (2021) 109066.
[34] M. Ta¸s and B. Yılmaz, Super resolution convolutional neural network based pre-processing for automatic polyp
detection in colonoscopy images, Comput. Electr. Eng., 90 (2021) 106959.
[35] A. Unnikrishnan, V. Sowmya and K.P. Soman, Deep AlexNet with reduced number of trainable parameters for
satellite image classification, Procedia Comput. Sci. , 143 (2018) 931-938.
[36] H.H. Vo and A. Verma, New deep neural nets for fine-grained diabetic retinopathy recognition on hybrid color
space, IEEE Int. Symp. Multimedia, Dec. 2016.
[37] S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng and G. Yang, Hierarchical retinal blood vessel segmentation based on
feature and ensemble learning , Neurocomputing, 149 (2015) 708-717.
[38] J. Wang , Q. Liu, H. Xie, Z. Yang and H. Zhou, Boosted EfficientNet: Detection of Lymph Node Metastases in
Breast Cancer Using Convolutional Neural Networks, Cancers 2021, 13, 661. (2021).
[39] J. Wang and R. Yuan, Automated diagnosis of neonatal encephalopathy on aEEG using deep neural networks,
Neurocomputing, 398 (2020), 95-107.
[40] J. Wei, Y. Ibrahim, S. Qian, H. Wang, G. Liu and Q. Yu, Analyzing the impact of soft errors in VGG networks
implemented on GPUs, Microelectron. Reliab., 110 (2020) 113648.
[41] N. Wu, J. Phang, J. Park, Y. Shen, Z. Huang, M. Zorin and K.J. Geras, Deep neural networks improve radiologists’
performance in breast cancer screening, IEEE Trans. Med. Imaging , 39 (4 )(2019) 1184-1194.
[42] B. Yin , C. Wang and F. Abza, New brain tumor classification method based on an improved version of whale
optimization algorithm,Biomed. Signal Process. Control, 56 (2020) 101728.
[43] M. Yousefi, A. Krzy˙zak and C.Y. Suen, Mass detection in digital breast tomosynthesis data using convolutional
neural networks and multiple instance learning, Comput. Biol. Med., 96 (2018) 283-293.
[44] W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao and Y. Rui. Visualizing and comparing AlexNet and VGG using
deconvolutional layers, Proc. 33 rd Inter. Conf. Mach. Learn., 2016.
extract organelles and macromolecules, J. Struct. Biol., 200 (2) (2017) 73-86.
[2] Sh. Amemiya, H. Takao and Sh. Kato, Automatic detection of brain metastases on contrast-enhanced CT with
deep-learning feature-fused single-shot detectors, Eur. J. Radiol., 136 (2021) 109577.
[3] A. Arjmand, S. Meshgini and R. Afrouzian, Breast Tumor Detection using Convolutional Neural Network in MRI
Images, J. Adv. Signal Process., 3 (2) (2020) 109-117.
[4] S. Bazrafkan and P. Corcoran, Semi-parallel deep neural networks (SPDNN), convergence and generalization,
arXiv preprint arXiv:1711.01963 (2017).
[5] M. Cadoni , A. Lagorio and E. Grosso, Incremental models based on features persistence for object recognition,Pattern Recognit. Lett. , 122 (2019) 38-44.
[6] C. Caradu, B. Spampinato and A.Maria, Fully automatic volume segmentation of infrarenal abdominal aortic
aneurysm computed tomography images with deep learning approaches versus physician controlled manual segmentation, J. Vasc. Surg. , 74(1) (2021) 246-256.
[7] G. Carneiro , J. Nascimento and A.P. Bradley, Unregistered multiview mammogram analysis with pre-trained deep
learning models, Int. Conf. Med. Image Comput. Comput. Assisted Intervention , Springer, Cham, 2015.
[8] E. Castro, J.S. Cardoso and J.C. Pereira, Elastic deformations for data augmentation in breast cancer mass
detection, 2018 IEEE EMBS Int. Conf. Biomed. Health. Inf. (BHI). IEEE, 2018.
[9] J. Dolz, Ch. Desrosiers, L. Wang, J.Yuan, D. Shen and I.Ayed, Deep CNN ensembles and suggestive annotations
for infant brain MRI segmentation, Comput. Med. Imaging Graphics, 79 (2020) 101660.
[10] S. Ebrahimkhani and M. Hisham Jaward, A review on segmentation of knee articular cartilage: from conventional
methods towards deep learning, Artif. Intell. Med., 106 (2020) 101851.
[11] M.M. Eltoukhy and I. Faye, An adaptive threshold method for mass detection in mammographic images, 2013
IEEE Inter. Conf. Signal and Image Processing Appl.. IEEE, 2013.
[12] V. Fischer, J. K¨ohler and T. Pfeil, The streaming rollout of deep networks-towards fully model-parallel execution,
arXiv preprint arXiv:1806.04965 (2018).
[13] S.V. Fotin, Y. Yin, H. Haldankar, J.W. Hoffmeister and S. Periaswamy, Detection of soft tissue densities from
digital breast tomosynthesis: comparison of conventional and deep learning approaches, Med. Imaging 2016:
Computer-Aided Diagnosis. Vol. 9785. International Society for Optics and Photonics, 2016.
[14] M.H. Gholizadeh , H. Ghayoumi Zadeh, H. Fatehi Marj and N. Ahmadi Nejad, The Investigation of Deep
Convolutional Neural Network for Diagnosing Breast Cancer in Thermographic Images, Jundishapur Sci. Med.
J., 18 (6) (2020) 615-629.
[15] G. Hamed, M.A.E.R. Marey, S.E.S. Amin and M.F. Tolba, Deep learning in breast cancer detection and classification, Int. Conf. Artif. Intell. Comput. Vision, Springer, Cham, 2020.
[16] A. Hassan, F. Liu, F. Wang and Y. Wang, Secure content based image retrieval for mobile users with deep neural
networks in the cloud,J. Syst. Archit. , 116 (2021) 102043.
[17] B. W. Hong and B.S. Sohn, Segmentation of regions of interest in mammograms in a topographic approach, IEEE
Trans. Inf. Technol. Biomedicine, 14(1) (2009) 129-139.
[18] A. Karsaz and S. Mohammadian Roshan, Medical image processing using deep convolutional neural networks,
Electr. Asre Mag., 5 (11) (2019) 23-28.
[19] T. Liang and Edward Y. Lee, Interstitial Lung Diseases in Children, Adolescents, and Young Adults: Different
from Infants and Older Adults, Radiologic Clin. , 58( 3) (2020) 487-502.
[20] W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu and F.E. Alsaadi, A survey of deep neural network architectures and
their applications, Neurocomputing, 234 (2017) 11-26.
[21] S. Liuhanen, M. Sallisalmi, V. Pettil¨a, N. Oksala and J. Tenhunen, Indirect measurement of the vascular endothelial glycocalyx layer thickness in human submucosal capillaries with a plug-in for ImageJ, Comput. Methods
Programs Biomed. , 110(1) (2013) 38-47.
[22] T. Masuda, T. Nakaura, Y. Funama, K. Sugino, T. Sato, T. Yoshiura, Y. Baba and K. Awai, Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies,
Radiography (2021).
[23] D. Mishkin, N. Sergievskiy and J. Matas, Systematic evaluation of convolution neural network advances on the
imagenet,Comput. Vision Image Understanding , 161 (2017) 11-19.
[24] Y. Mori, Sh. Kudo and J. East, Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an
add-on analysis of a clinical trial (with video), Gastrointestinal endoscopy, 92 (4) (2020) 905-911.
[25] B. Mughal, M. Sharif, N. Muhammad and T. Saba, A novel classification scheme to decline the mortality rate
among women due to breast tumor, Microsc. Res. Tech. , 81(2) (2018) 171-180.[26] H. Pratt, F. Coenen, D.M. Broadbent, S.P. Harding and Y. Zheng, Convolutional neural networks for diabetic
retinopathy, Inter. Conf. Med. Imaging Understanding Anal., July 2016.
[27] Y. M. S. Reddy, R.E. Ravindran and K.H. Kishore, Diabetic retinopathy through retinal image analysis: A review,
Inter. J. Eng. Technol., 7(1.5) (2018) 19-25.
[28] M. Salvi, U. Rajendra Acharya, Filippo Molinari and Kristen M. Meiburger, The impact of pre-and post-image
processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis,
Comput. Biol. Med., (2020) 104129.
[29] N. Sharma, V. Jain and A. Mishra, An analysis of convolutional neural networks for image classification,Procedia
Comput. Sci., 132 (2018) 377-384.
[30] S. Suzuki, X. Zhang, N. Homma, K. Ichiji, N. Sugita, Y. Kawasumi, T. Ishibashi and M. Yoshizawa, Mass
detection using deep convolutional neural network for mammographic computer-aided diagnosis, 2016 55th Annu.
Conf. Soc. Instrum. Control Eng. Japan (SICE). IEEE, 2016.
[31] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich,
Going deeper with convolutions, Proc. IEEE conf. comput. vision and pattern recognition 2015, pp. 1-9.
[32] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, Rethinking the inception architecture for computer
vision , Proc. IEEE conf. comput. vision and pattern recognition. 2016.
[33] E. Takaya, Y. Takeichi, M. Ozaki and S. Kurihara, Sequential semi-supervised segmentation for serial electron
microscopy image with small number of labels, J. Neurosci. Methods , 351 (2021) 109066.
[34] M. Ta¸s and B. Yılmaz, Super resolution convolutional neural network based pre-processing for automatic polyp
detection in colonoscopy images, Comput. Electr. Eng., 90 (2021) 106959.
[35] A. Unnikrishnan, V. Sowmya and K.P. Soman, Deep AlexNet with reduced number of trainable parameters for
satellite image classification, Procedia Comput. Sci. , 143 (2018) 931-938.
[36] H.H. Vo and A. Verma, New deep neural nets for fine-grained diabetic retinopathy recognition on hybrid color
space, IEEE Int. Symp. Multimedia, Dec. 2016.
[37] S. Wang, Y. Yin, G. Cao, B. Wei, Y. Zheng and G. Yang, Hierarchical retinal blood vessel segmentation based on
feature and ensemble learning , Neurocomputing, 149 (2015) 708-717.
[38] J. Wang , Q. Liu, H. Xie, Z. Yang and H. Zhou, Boosted EfficientNet: Detection of Lymph Node Metastases in
Breast Cancer Using Convolutional Neural Networks, Cancers 2021, 13, 661. (2021).
[39] J. Wang and R. Yuan, Automated diagnosis of neonatal encephalopathy on aEEG using deep neural networks,
Neurocomputing, 398 (2020), 95-107.
[40] J. Wei, Y. Ibrahim, S. Qian, H. Wang, G. Liu and Q. Yu, Analyzing the impact of soft errors in VGG networks
implemented on GPUs, Microelectron. Reliab., 110 (2020) 113648.
[41] N. Wu, J. Phang, J. Park, Y. Shen, Z. Huang, M. Zorin and K.J. Geras, Deep neural networks improve radiologists’
performance in breast cancer screening, IEEE Trans. Med. Imaging , 39 (4 )(2019) 1184-1194.
[42] B. Yin , C. Wang and F. Abza, New brain tumor classification method based on an improved version of whale
optimization algorithm,Biomed. Signal Process. Control, 56 (2020) 101728.
[43] M. Yousefi, A. Krzy˙zak and C.Y. Suen, Mass detection in digital breast tomosynthesis data using convolutional
neural networks and multiple instance learning, Comput. Biol. Med., 96 (2018) 283-293.
[44] W. Yu, K. Yang, Y. Bai, T. Xiao, H. Yao and Y. Rui. Visualizing and comparing AlexNet and VGG using
deconvolutional layers, Proc. 33 rd Inter. Conf. Mach. Learn., 2016.
)
Volume 12, Special Issue
December 2021Pages 1269-1282
- Receive Date: 14 March 2021
- Accept Date: 24 August 2021