ISSN 2353-6977 (Online)
Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%.
- APA 7th style
Al-Huseiny, M. S., & Sajit, A. S. (2022). Breast cancer CAD system by using transfer learning and enhanced ROI. Applied Computer Science, 18(1), 99-111. https://doi.org/10.23743/acs-2022-08
- Chicago style
Al-Huseiny, Muayed S, and Ahmed S Sajit. "Breast Cancer Cad System by Using Transfer Learning and Enhanced Roi." Applied Computer Science 18, no. 1 (2022): 99-111.
- IEEE style
M. S. Al-Huseiny and A. S. Sajit, "Breast cancer CAD system by using transfer learning and enhanced ROI," Applied Computer Science, vol. 18, no. 1, pp. 99-111, 2022, doi: 10.23743/acs-2022-08.
- Vancouver style
Al-Huseiny MS, Sajit AS. Breast cancer CAD system by using transfer learning and enhanced ROI. Applied Computer Science. 2022;18(1):99-111.
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