Research Article
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Year 2023, Volume: 9 Issue: 4, 359 - 367, 31.12.2023

Yasin Kırelli Gizem Aydın

Abstract

References

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  • [2] Liu, M., Hu, L., Tang, Y., Wang, C., He, Y., Zeng, C., ... & Huo, W. (2022). A Deep Learning Method for Breast Cancer Classification in the Pathology Images, IEEE Journal of Biomedical Health Informatics, vol. 26, no. 10, pp. 5025–5032, doi: 10.1109/JBHI.2022.3187765.
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  • [8] Zhou, X., Li, Y., Gururajan, R., Bargshady, G., Tao, X., Venkataraman, R., ... & Kondalsamy-Chennakesavan, S. (2020, November). A new deep convolutional neural network model for automated breast Cancer detection. In 2020 7th International Conference on Behavioural and Social Computing (BESC) (pp. 1-4), doi: 10.1109/BESC51023.2020.9348322.
  • [9] Karthiga, R., & Narasimhan, K. (2018, March). Automated diagnosis of breast cancer using wavelet based entropy features. In 2018 Second international conference on electronics, communication and aerospace technology (ICECA) (pp. 274-279), doi: 10.1109/ICECA.2018.8474739.
  • [10] Xiang, Z., Ting, Z., Weiyan, F., & Cong, L. (2019, June). Breast cancer diagnosis from histopathological image based on deep learning. In 2019 Chinese Control And Decision Conference (CCDC) (pp. 4616-4619), doi: 10.1109/CCDC.2019.8833431.
  • [11] Gour, M., Jain, S., & Sunil Kumar, T. (2020). Residual learning based CNN for breast cancer histopathological image classification. International Journal of Imaging Systems and Technology, 30(3), 621-635, doi: 10.1002/ima.22403.
  • [12] Gupta, K., & Chawla, N. (2020). Analysis of histopathological images for prediction of breast cancer using traditional classifiers with pre-trained CNN. Procedia Computer Science, 167, 878-889, doi: 10.1016/j.procs.2020.03.427.
  • [13] “Breast Cancer Treatment,” National Cancer Instıtute, 2023. https://www.cancer.gov/types/breast/patient/breast-treatment-pdq.
  • [14] “Breast Cancer,” World Health Organization, 2021. https://www.who.int/news-room/fact-sheets/detail/breast-cancer.
  • [15] DeSantis, C. E., Bray, F., Ferlay, J., Lortet-Tieulent, J., Anderson, B. O., & Jemal, A. (2015). International variation in female breast cancer incidence and mortality rates. Cancer epidemiology, biomarkers & prevention, 24(10), 1495-1506, doi: 10.1158/1055-9965.EPI-15-0535.
  • [16] Anderson, B. O., Braun, S., Lim, S., Smith, R. A., Taplin, S., & Thomas, D. B. (2003). Early detection of breast cancer in countries with limited resources. The breast journal, 9, S51-S59, doi: 10.1046/j.1524-4741.9.s2.4.x.
  • [17] Kösters, J. P., Gøtzsche, P. C., & Cochrane Breast Cancer Group. (1996). Regular self‐examination or clinical examination for early detection of breast cancer. Cochrane Database of Systematic Reviews, 2010(1)., doi: 10.1002/14651858.CD003373.
  • [18] Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016, July). Breast cancer histopathological image classification using convolutional neural networks. In 2016 international joint conference on neural networks (IJCNN) (pp. 2560-2567), doi: 10.1109/IJCNN.2016.7727519.
  • [19] Zheng, D., He, X., & Jing, J. (2023). Overview of artificial intelligence in breast cancer medical imaging. Journal of Clinical Medicine, 12(2), 419, doi: 10.3390/jcm12020419.
  • [20] Evans, K. K., Birdwell, R. L., & Wolfe, J. M. (2013). If you don’t find it often, you often don’t find it: why some cancers are missed in breast cancer screening. PloS one, 8(5), e64366., doi: 10.1371/journal.pone.0064366.
  • [21] Cheng, J. Z., Ni, D., Chou, Y. H., Qin, J., Tiu, C. M., Chang, Y. C., ... & Chen, C. M. (2016). Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Scientific reports, 6(1), 24454.
  • [22] Esteva, A., Kuprel, B., Novoa, R. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 115–118 (2017). https://doi.org/10.1038/nature21056.
  • [23] Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2015). A dataset for breast cancer histopathological image classification. Ieee transactions on biomedical engineering, 63(7), 1455-1462, doi: 10.1109/TBME.2015.2496264.
  • [24] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • [25] Sewak, M., Karim, M. R., & Pujari, P. (2018). Practical convolutional neural networks: implement advanced deep learning models using Python. Packt Publishing Ltd.
  • [26] ERDEM, E., & AYDİN, T. (2021). Göğüs Kanseri Histopatolojik Görüntü Sınıflandırması. Bilişim Teknolojileri Dergisi, 14(1), 87-94, doi: 10.17671/gazibtd.746673.
  • [27] A. Rastogi, “ResNet50.” https://blog.devgenius.io/resnet50-6b42934db431.
  • [28] S. Mukherjee, “The Annotated ResNet-50.” https://towardsdatascience.com/the-annotated-resnet-50-a6c536034758.
  • [29] Al Husaini, M. A. S., Habaebi, M. H., Gunawan, T. S., Islam, M. R., & Hameed, S. A. (2021, June). Automatic breast cancer detection using inception V3 in thermography. In 2021 8th International Conference on Computer and Communication Engineering (ICCCE) (pp. 255-258), doi: 10.1109/ICCCE50029.2021.9467231.
  • [30] Cao, J., Yan, M., Jia, Y., Tian, X., & Zhang, Z. (2021). Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals. EURASIP Journal on Advances in Signal Processing, 2021(1), 1-25., doi: 10.1186/s13634-021-00740-8.
  • [31] Mujahid, M., Rustam, F., Álvarez, R., Luis Vidal Mazón, J., Díez, I. D. L. T., & Ashraf, I. (2022). Pneumonia classification from X-ray images with inception-V3 and convolutional neural network. Diagnostics, 12(5), 1280 , doi: 10.3390/diagnostics12051280.
  • [32] Mednikov, Y., Nehemia, S., Zheng, B., Benzaquen, O., & Lederman, D. (2018, July). Transfer representation learning using Inception-V3 for the detection of masses in mammography. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2587-2590). IEEE., doi: 10.1109/EMBC.2018.8512750.
  • [33] Liu, Z., Yang, C., Huang, J., Liu, S., Zhuo, Y., & Lu, X. (2021). Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer. Future Generation Computer Systems, 114, 358-367, doi: 10.1016/j.future.2020.08.015.

Classification of Histopathological Images in Automatic Detection of Breast Cancer with Deep Learning Approach

Year 2023, Volume: 9 Issue: 4, 359 - 367, 31.12.2023

Yasin Kırelli Gizem Aydın

Abstract

Convolutional neural networks have emerged as an essential tool for image classification and object detection. In the health field, these tools are a crucial factor in saving time and minimizing the margin of error for the health system and employees. Breast cancer is the most common type of cancer in women worldwide. In many cases, it can threaten human life, resulting in death. Although methods have been developed for the early diagnosis of this health problem, its support with digital systems remains incomplete. In diagnosis, histopathological images are examined with microscope methods. In cases where the number of pathologies is insufficient, delay problems may occur and the error rate increases in manual controls. The study aims to design a deep-learning object detection method for the pre-detection of breast cancer. The publicly published BreaKHis dataset is used as the dataset. Model results that generated with VGG16, InceptionV3 and ResNet50 deep learning architectures have been compared. The highest accuracy rate have been obtained with the proposed model as 85%. Accuracy, AUC, precision, recall, F-score performance metrics have been analyzed for each model. A decision support system screen design has been created using the proposed model weight file. With the study, the computer-assisted clinical support system makes clinicians' life more manageable and recommends early diagnosis.

Keywords

Deep Learning VGG16 Inceptionv3 Resnet50 BreakHis

References

  • [1] Abdar, M., Zomorodi-Moghadam, M., Zhou, X., Gururajan, R., Tao, X., Barua, P. D., & Gururajan, R. (2020). A new nested ensemble technique for automated diagnosis of breast cancer, Pattern Recognition Letters, vol. 132, pp. 123–131, doi: 10.1016/j.patrec.2018.11.004.
  • [2] Liu, M., Hu, L., Tang, Y., Wang, C., He, Y., Zeng, C., ... & Huo, W. (2022). A Deep Learning Method for Breast Cancer Classification in the Pathology Images, IEEE Journal of Biomedical Health Informatics, vol. 26, no. 10, pp. 5025–5032, doi: 10.1109/JBHI.2022.3187765.
  • [3] Nahid, A. A., & Kong, Y. (2017). Involvement of machine learning for breast cancer image classification: a survey. Computational and mathematical methods in medicine, 2017., doi: 10.1155/2017/3781951.
  • [4] Parvin, F., & Hasan, M. A. M. (2020, June). A comparative study of different types of convolutional neural networks for breast cancer histopathological image classification. In 2020 IEEE Region 10 Symposium (TENSYMP) (pp. 945-948), doi: 10.1109/TENSYMP50017.2020.9230787.
  • [5] Jiang, Y., Chen, L., Zhang, H., & Xiao, X. (2019). Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PloS one, 14(3), e0214587, doi: 10.1371/journal.pone.0214587.
  • [6] Giaquinto, A. N., Miller, K. D., Tossas, K. Y., Winn, R. A., Jemal, A., & Siegel, R. L. (2022). Cancer statistics for African American/black people 2022. CA: a cancer journal for clinicians, 72(3), 202-229, doi: 10.3322/caac.21718.
  • [7] Kakde, A., Arora, N., & Sharma, D. (2019). A comparative study of different types of cnn and highway cnn techniques. Global Journal of Engineering Science and Research Management, 6(4), 18-31, doi: 10.5281/zenodo.2639265.
  • [8] Zhou, X., Li, Y., Gururajan, R., Bargshady, G., Tao, X., Venkataraman, R., ... & Kondalsamy-Chennakesavan, S. (2020, November). A new deep convolutional neural network model for automated breast Cancer detection. In 2020 7th International Conference on Behavioural and Social Computing (BESC) (pp. 1-4), doi: 10.1109/BESC51023.2020.9348322.
  • [9] Karthiga, R., & Narasimhan, K. (2018, March). Automated diagnosis of breast cancer using wavelet based entropy features. In 2018 Second international conference on electronics, communication and aerospace technology (ICECA) (pp. 274-279), doi: 10.1109/ICECA.2018.8474739.
  • [10] Xiang, Z., Ting, Z., Weiyan, F., & Cong, L. (2019, June). Breast cancer diagnosis from histopathological image based on deep learning. In 2019 Chinese Control And Decision Conference (CCDC) (pp. 4616-4619), doi: 10.1109/CCDC.2019.8833431.
  • [11] Gour, M., Jain, S., & Sunil Kumar, T. (2020). Residual learning based CNN for breast cancer histopathological image classification. International Journal of Imaging Systems and Technology, 30(3), 621-635, doi: 10.1002/ima.22403.
  • [12] Gupta, K., & Chawla, N. (2020). Analysis of histopathological images for prediction of breast cancer using traditional classifiers with pre-trained CNN. Procedia Computer Science, 167, 878-889, doi: 10.1016/j.procs.2020.03.427.
  • [13] “Breast Cancer Treatment,” National Cancer Instıtute, 2023. https://www.cancer.gov/types/breast/patient/breast-treatment-pdq.
  • [14] “Breast Cancer,” World Health Organization, 2021. https://www.who.int/news-room/fact-sheets/detail/breast-cancer.
  • [15] DeSantis, C. E., Bray, F., Ferlay, J., Lortet-Tieulent, J., Anderson, B. O., & Jemal, A. (2015). International variation in female breast cancer incidence and mortality rates. Cancer epidemiology, biomarkers & prevention, 24(10), 1495-1506, doi: 10.1158/1055-9965.EPI-15-0535.
  • [16] Anderson, B. O., Braun, S., Lim, S., Smith, R. A., Taplin, S., & Thomas, D. B. (2003). Early detection of breast cancer in countries with limited resources. The breast journal, 9, S51-S59, doi: 10.1046/j.1524-4741.9.s2.4.x.
  • [17] Kösters, J. P., Gøtzsche, P. C., & Cochrane Breast Cancer Group. (1996). Regular self‐examination or clinical examination for early detection of breast cancer. Cochrane Database of Systematic Reviews, 2010(1)., doi: 10.1002/14651858.CD003373.
  • [18] Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016, July). Breast cancer histopathological image classification using convolutional neural networks. In 2016 international joint conference on neural networks (IJCNN) (pp. 2560-2567), doi: 10.1109/IJCNN.2016.7727519.
  • [19] Zheng, D., He, X., & Jing, J. (2023). Overview of artificial intelligence in breast cancer medical imaging. Journal of Clinical Medicine, 12(2), 419, doi: 10.3390/jcm12020419.
  • [20] Evans, K. K., Birdwell, R. L., & Wolfe, J. M. (2013). If you don’t find it often, you often don’t find it: why some cancers are missed in breast cancer screening. PloS one, 8(5), e64366., doi: 10.1371/journal.pone.0064366.
  • [21] Cheng, J. Z., Ni, D., Chou, Y. H., Qin, J., Tiu, C. M., Chang, Y. C., ... & Chen, C. M. (2016). Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Scientific reports, 6(1), 24454.
  • [22] Esteva, A., Kuprel, B., Novoa, R. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 115–118 (2017). https://doi.org/10.1038/nature21056.
  • [23] Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2015). A dataset for breast cancer histopathological image classification. Ieee transactions on biomedical engineering, 63(7), 1455-1462, doi: 10.1109/TBME.2015.2496264.
  • [24] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • [25] Sewak, M., Karim, M. R., & Pujari, P. (2018). Practical convolutional neural networks: implement advanced deep learning models using Python. Packt Publishing Ltd.
  • [26] ERDEM, E., & AYDİN, T. (2021). Göğüs Kanseri Histopatolojik Görüntü Sınıflandırması. Bilişim Teknolojileri Dergisi, 14(1), 87-94, doi: 10.17671/gazibtd.746673.
  • [27] A. Rastogi, “ResNet50.” https://blog.devgenius.io/resnet50-6b42934db431.
  • [28] S. Mukherjee, “The Annotated ResNet-50.” https://towardsdatascience.com/the-annotated-resnet-50-a6c536034758.
  • [29] Al Husaini, M. A. S., Habaebi, M. H., Gunawan, T. S., Islam, M. R., & Hameed, S. A. (2021, June). Automatic breast cancer detection using inception V3 in thermography. In 2021 8th International Conference on Computer and Communication Engineering (ICCCE) (pp. 255-258), doi: 10.1109/ICCCE50029.2021.9467231.
  • [30] Cao, J., Yan, M., Jia, Y., Tian, X., & Zhang, Z. (2021). Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals. EURASIP Journal on Advances in Signal Processing, 2021(1), 1-25., doi: 10.1186/s13634-021-00740-8.
  • [31] Mujahid, M., Rustam, F., Álvarez, R., Luis Vidal Mazón, J., Díez, I. D. L. T., & Ashraf, I. (2022). Pneumonia classification from X-ray images with inception-V3 and convolutional neural network. Diagnostics, 12(5), 1280 , doi: 10.3390/diagnostics12051280.
  • [32] Mednikov, Y., Nehemia, S., Zheng, B., Benzaquen, O., & Lederman, D. (2018, July). Transfer representation learning using Inception-V3 for the detection of masses in mammography. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2587-2590). IEEE., doi: 10.1109/EMBC.2018.8512750.
  • [33] Liu, Z., Yang, C., Huang, J., Liu, S., Zhuo, Y., & Lu, X. (2021). Deep learning framework based on integration of S-Mask R-CNN and Inception-v3 for ultrasound image-aided diagnosis of prostate cancer. Future Generation Computer Systems, 114, 358-367, doi: 10.1016/j.future.2020.08.015.
There are 33 citations in total.

Details

Primary Language English
Subjects Surgery (Other)
Journal Section Research Article
Authors

Yasin Kırelli 0000-0002-3605-8621

Gizem Aydın 0000-0001-6353-0648

Early Pub Date November 27, 2023
Publication Date December 31, 2023
Submission Date July 25, 2023
Acceptance Date November 13, 2023
Published in Issue Year 2023 Volume: 9 Issue: 4

Cite

APA Kırelli, Y., & Aydın, G. (2023). Classification of Histopathological Images in Automatic Detection of Breast Cancer with Deep Learning Approach. International Journal of Computational and Experimental Science and Engineering, 9(4), 359-367.
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