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Breast Cancer Detection Using Deep Multilayer Neural Networks | ||
| Journal of Epigenetics | ||
| دوره 3، شماره 1، خرداد 2022، صفحه 27-34 اصل مقاله (901.39 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22111/jep.2022.41712.1041 | ||
| نویسندگان | ||
| Mohammad Mehdi Keikha* ؛ Yahya Kord Tamandani | ||
| Department of Computer Science, University of Sistan and Baluchestan, Iran, Zahedan | ||
| چکیده | ||
| Breast cancer is the most common cancer among women and is the second leading cause of death. There is currently no efficient way to prevent breast cancer, but its detection in early stages can increase the patient's chances of being cured and surviving. Computer-aided diagnosis (CAD) systems, based on image processing techniques, can provide a more reliable interpretation of mammographic images to detect microcalcifications and have been able to identify and classify benign and malignant tumors. If we are dealing with a massive number of images, this system increases the ability and accuracy of detection. Also, in cases where the number of images is not large, CAD systems can significantly improve the image quality. In addition, a CAD system can identify suspicious areas to provide radiologists with a visual aid to interpret mammograms. Deep learning and convolutional neural networks have recently shown significant performance for visual applications. Convolutional neural networks have also been used efficiently to analyze medical images and diagnose mammograms. In this paper, a CAD system based on convolutional neural networks (Mask R-CNN) with multi-task learning to detect breast cancer and segment mammogram images is proposed. The Mask-RCNN technique is one of the strongest and most flexible deep grids ever designed for machine vision. In this article, multitask learning with the integration of two tasks of classification and segmentation is used to diagnose breast cancer. R-CNN convolution neural network is used to diagnose cancerous mass. This system consists of two main stages, including the production of pseudo-color image and segmentation-detection based on convolutional neural networks (R-CNN Mask). The INbreast dataset is employed for evaluation of the proposed method. | ||
| کلیدواژه‌ها | ||
| Multitask learning؛ Deep learning؛ Convolutional Neural Network؛ Breast cancer؛ CAD | ||
| مراجع | ||
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Chauhan, R., Kaur, H., & Alam, M. A. (2010). Data clustering method for discovering clusters in spatial cancer databases. International Journal of Computer Applications, 10(6), 9-14.
De Cea, M. V. S., Diedrich, K., Bakalo, R., Ness, L., & Richmond, D. (2020, October). Multi-task Learning for Detection and Classification of Cancer in Screening Mammography. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 241-250). Springer, Cham.
Dhungel, N., Carneiro, G., & Bradley, A. P. (2017). A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Medical image analysis, 37, 114-128.
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).
Karpathy, A. (2016). Cs231n convolutional neural networks for visual recognition. Neural networks, 1(1).
Le, T. L. T., Thome, N., Bernard, S., Bismuth, V., & Patoureaux, F. (2019). Multitask classification and segmentation for cancer diagnosis in mammography. arXiv preprint arXiv:1909.05397.
Li, Y., Chen, H., Zhang, L., & Cheng, L. (2018, August). Mammographic mass detection based on convolution neural network. In 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 3850-3855). IEEE.
Liao, Q., Ding, Y., Jiang, Z. L., Wang, X., Zhang, C., & Zhang, Q. (2019). Multi-task deep convolutional neural network for cancer diagnosis. Neurocomputing, 348, 66-73.
López-García, G., Jerez, J. M., Franco, L., & Veredas, F. J. (2020). Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data. PloS one, 15(3), e0230536.
Min, H., Chandra, S. S., Crozier, S., & Bradley, A. P. (2019). Multi-scale sifting for mammographic mass detection and segmentation. Biomedical Physics & Engineering Express, 5(2), 025022.
Mostavi, M., Chiu, Y. C., Huang, Y., & Chen, Y. (2020). Convolutional neural network models for cancer type prediction based on gene expression. BMC Medical Genomics, 13, 1-13.
Oliveira, H. S., Teixeira, J. F., & Oliveira, H. P. (2019, September). Lightweight deep learning pipeline for detection, segmentation and classification of breast cancer anomalies. In International Conference on Image Analysis and Processing (pp. 707-715). Springer, Cham.
Samala, R. K., Chan, H. P., Hadjiiski, L. M., Helvie, M. A., Cha, K. H., & Richter, C. D. (2017). Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Physics in Medicine & Biology, 62(23), 8894.
Venkatesan, R., & Ganesh, A. B. (2018). Deep recurrent neural networks based binaural speech segregation for the selection of closest target of interest. Multimedia Tools and Applications.
Weinland, D., Ronfard, R., & Boyer, E. (2011). A survey of vision-based methods for action representation, segmentation and recognition. Computer vision and image understanding, 115(2), 224-241.
Yahya Kord Tamandani (2020) Early Detection of Breast Cancer in Women Using a Cost-effective Procedure. journal of Epgenetics Volume 2, Issue 1, Pages 1-6 | ||
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