Mohamed, A., Amer, E., Noor Eldin, S., khaled, J., Hossam, M., Elmasry, N., Adnan, G. (2022). The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning. Journal of Computing and Communication, 1(1), 27-37. doi: 10.21608/jocc.2022.218453
Ammar Mohamed; Eslam Amer; sara Noor Eldin; jana khaled; Maysoon Hossam; Noha Elmasry; Ganna Tamer Adnan. "The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning". Journal of Computing and Communication, 1, 1, 2022, 27-37. doi: 10.21608/jocc.2022.218453
Mohamed, A., Amer, E., Noor Eldin, S., khaled, J., Hossam, M., Elmasry, N., Adnan, G. (2022). 'The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning', Journal of Computing and Communication, 1(1), pp. 27-37. doi: 10.21608/jocc.2022.218453
Mohamed, A., Amer, E., Noor Eldin, S., khaled, J., Hossam, M., Elmasry, N., Adnan, G. The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning. Journal of Computing and Communication, 2022; 1(1): 27-37. doi: 10.21608/jocc.2022.218453
The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning
1Faculty of Graduate Studies for Statistical Research Cairo University
2Misr International university
3Department of Computer Science, Misr International University, Cairo, Egypt
4Department of Computer Science ,Faculty of Computer Science , Misr International University, Cairo, Egypt
5Department of Computer Science , Faculty of Computer Science , Misr International University , Cairo , Egypt
Abstract
According to the World Health Organization, cancer is the second leading cause of mortality. Breast cancer is the most prevalent cancer diagnosed in women around the world. Breast cancer diagnostics range from mammograms to CT scans and ultrasounds, but a biopsy is the only way to know for sure if the suspicious cells detected in the breast are cancerous or not. This paper’s main contribution is multi-fold. First, it proposes a deep learning approach to detect breast cancer from biopsy microscopy images. Deep convolution nets of various types are used. Second, the paper examines the effects of different data preprocessing techniques on the performance of deep learning models. Third, the paper introduces an ensemble method for aggregating the best models in order to improve performance. The experimental results revealed that Densenet169, Resnet50, and Resnet101 are the three best models achieving accuracy scores of 62%, 68%, and 85%, respectively. without data preprocessing. With the help of data augmentation and segmentation, the accuracy of these models increased by 20%, 17%, and 6%, respectively. Additionally, the ensemble learning technique improves the accuracy of the models even further. The results show that the best accuracy achieved is 92.5%.
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