Deep Learning-Based Classification of Histopathology Images for Cancer Diagnosis
Keywords:
Invasive ductal carcinoma (IDC), convolutional-neural network (CNN), Kaggle databaseAbstract
In this study, we apply deep learning techniques to create a complete CAD system for accurate and efficient IDC/non-IDC categorization. This CAD system has two distinct classification methods: machine learning-based classification using a variety of classifiers and deep learning-based classification using a specially constructed convolutional-neural network (CNN). Kaggle, a publicly accessible benchmark database, is used to accomplish this study. Accuracy, sensitivity, specificity, false positive rate, classification error, and precision are only few of the performance metrics used to assess machine learning and deep learning classifiers. Accuracy and sensitivity are selected as the primary characteristics by which the best classifier is evaluated. The purpose of this new saliency detection method is to aid in the diagnosis of invasive ductal carcinoma (IDC) by using IDC histopathology pictures to train deep learning algorithms.
Downloads
References
BabaAhmadi, S. Khalafi and F. M. Esfahani, "Designing an Improved Deep Learning-Based Classifier for Breast Cancer Identification in Histopathology Images," 2022 International Conference on Machine Vision and Image Processing (MVIP), Ahvaz, Iran, Islamic Republic of, 2022, pp. 1-4, doi: 10.1109/MVIP53647.2022.9738774.
S. Kushwaha, M. Adil, M. Abuzar, A. Nazeer and S. K. Singh, "Deep learning-based model for breast cancer histopathology image classification," 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, 2021, pp. 539-543, doi: 10.1109/ICIEM51511.2021.9445319.
Y. Yari and H. Nguyen, "A State-of-the-art Deep Transfer Learning-Based Model for Accurate Breast Cancer Recognition in Histology Images," 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), Cincinnati, OH, USA, 2020, pp. 900-905, doi: 10.1109/BIBE50027.2020.00153.
C. P. Nguyen, A. Hoang Vo, and B. T. Nguyen, “Breast Cancer Histology Image Classification using Deep Learning,” Proc. - 2019 19th Int. Symp. Commun. Inf. Technol. Isc. 2019, pp. 366–370, 2019, doi: 10.1109/ISCIT.2019.8905196.
L. Conte, B. Tafuri, G. De Nunzio, M. Portaluri, and A. Galiano, “Breast Cancer Mass detection in DCE-MRI using Deep-Learning Features Followed by Discrimination of Infiltrative vs In Situ Carcinoma through a Machine-learning approach,” no. August, 2020, doi: 10.20944/preprints202008.0179.v1.
J. Dana, V. Agnus, F. Ouhmich, and B. Gallix, “Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective,” Semin. Nucl. Med., 2020, doi: 10.1053/j.semnuclmed.2020.07.003.
Q. Liu, Z. Liu, S. Yong, K. Jia, and N. Razmjooy, “Computer-aided breast cancer diagnosis based on image segmentation and interval analysis,” Automatika, vol. 61, no. 3, pp. 496–506, 2020, doi: 10.1080/00051144.2020.1785784.
Yadavendra and S. Chand, “A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method,” Mach. Vis. Appl., vol. 31, no. 6, pp. 1–10, 2020, doi: 10.1007/s00138-020- 01094-1.
R. Krithiga and P. Geetha, “Breast Cancer Detection, Segmentation and Classification on Histopathology Images Analysis: A Systematic Review,” Arch. Comput. Methods Eng., no. 0123456789, 2020, doi: 10.1007/s11831-020- 09470-w.
A. J. Navarrete-Welton and D. A. Hashimoto, “Current applications of artificial intelligence for intraoperative decision support in surgery,” Front. Med., 2020, doi: 10.1007/s11684-020-0784-7.
Y. Tolkach, T. Dohmgörgen, M. Toma, and G. Kristiansen, “High-accuracy prostate cancer pathology using deep learning,” Nat. Mach. Intell., vol. 2, no. 7, pp. 411–418, 2020, doi: 10.1038/s42256-020-0200-7.
H. Moghadas-Dastjerdi, H. R. Sha-E-Tallat, L. Sannachi, A. Sadeghi-Naini, and G. J. Czarnota, “A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning,” Sci. Rep., vol. 10, no. 1, pp. 1–11, 2020, doi: 10.1038/s41598-020- 67823-
W. Sanderink and R. Mann, “Minimally invasive breast cancer excision using the Breast Lesion Excision System under ultrasound guidance,” Submitted, no. 0123456789, 2020, doi: 10.1007/s10549-020-05814-z
C. E. Vera-Tizatl et al., “Cell morphology impact on the set-up of electroporation protocols for in-suspension and adhered breast cancer cells,” Electromagn. Biol. Med., vol. 00, no. 00, pp. 1–17, 2020, doi: 10.1080/15368378.2020.1799387.
H. Chapala, “ResNet : Detection of Invasive Ductal Carcinoma in Breast Histopathology Images Using Deep Learning,” no. Icesc, pp. 60–67, 2020
Naga Swetha, G. ., & M. Sandi, A. . (2023). A Brand-New, Area - Efficient Architecture for the FFT Algorithm Designed for Implementation of FPGAs. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 114–122. https://doi.org/10.17762/ijritcc.v11i2.6135
S. Ali, A. Tanveer, A. Hussain, and S. U. Rehman, “Identification of Cancer Disease Using Image Processing Approahes,” vol. 9, no. 2, pp. 6–15, 2020, doi: 10.11648/j.ijiis.20200902.11.
S. Robertson, B. Acs, M. Lippert, and J. Hartman, “Prognostic potential of automated Ki67 evaluation in breast cancer: different hot spot definitions versus true global score,” Breast Cancer Res. Treat., vol. 183, no. 1, pp. 161–175, 2020, doi: 10.1007/s10549-020-05752-w
K. Taunk et al., The development and clinical applications of proteomics: an Indian perspective, vol. 0, no. 0. Taylor & Francis, 2020
AGYEI , I. T. . (2021). Simulating HRM Technology Operations in Contemporary Retailing . International Journal of New Practices in Management and Engineering, 10(02), 10–14. https://doi.org/10.17762/ijnpme.v10i02.132
N. Maroof, A. Khan, S. A. Qureshi, A. ul Rehman, R. K. Khalil, and S. O. Shim, “Mitosis detection in breast cancer histopathology images using hybrid feature space,” Photodiagnosis Photodyn. Ther., vol. 31, p. 101885, 2020, doi: 10.1016/j.pdpdt.2020.101885
A. Aloyayri, “Breast Cancer Classification from Histopathological Images Using Transfer Learning and Deep Neural Networks School of Graduate Studies,” no. April, 2020
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
