Automated breast cancer diagnosis using deep learning and region of interest detection (BC-DROID)
ACM-BCB 2017 - Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Page: 536-543
2017
- 65Citations
- 36Usage
- 100Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations65
- Citation Indexes65
- 65
- CrossRef35
- Usage36
- Downloads35
- Abstract Views1
- Captures100
- Readers100
- 100
Conference Paper Description
Detection of suspicious regions in mammogram images and the subsequent diagnosis of these regions remains a challenging problem in the medical world. There still exists an alarming rate of misdiagnosis of breast cancer. This results in both over treatment through incorrect positive diagnosis of cancer and under treatment through overlooked cancerous masses. Convolutional neural networks have shown strong applicability to various image datasets, enabling detailed features to be learned from the data and, as a result, the ability to classify these images at extremely low error rates. In order to overcome the difficulty in diagnosing breast cancer from mammogram images, we propose our framework for automated breast cancer detection and diagnosis, called BC-DROID, which provides automated region of interest detection and diagnosis using convolutional neural networks. BC-DROID first pretrains based on physician-defined regions of interest in mammogram images. It then trains based on the full mammogram image. The resulting network is able to detect and classify regions of interest as cancerous or benign in one step. We demonstrate the accuracy of our framework's ability to both locate the regions of interest as well as diagnose them. Our framework achieves a detection accuracy of up to 90% and a classification accuracy of 93.5% (AUC of 92.315%). To the best of our knowledge, this is the first work enabling both automated detection and diagnosis of these areas in one step from full mammogram images. Using our framework's website, a user can upload a single mammogram image, visualize suspicious regions, and receive the automated diagnoses of these regions.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85031316917&origin=inward; http://dx.doi.org/10.1145/3107411.3107484; https://dl.acm.org/doi/10.1145/3107411.3107484; https://scholarsmine.mst.edu/comsci_facwork/1408; https://scholarsmine.mst.edu/cgi/viewcontent.cgi?article=2408&context=comsci_facwork; https://dx.doi.org/10.1145/3107411.3107484
Association for Computing Machinery (ACM)
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