Abstract
The incidence rate of breast cancer continued to rise in the last few decades. Current screening strategy of breast cancer is based on classic X-ray imaging. The sensitivity and specificity of the diagnosis are largely depend on the experiences of the radiologists, and uncertain diagnosis is quite frequent because of resolution limitations and the concerns of lawsuits arisen from wrong diagnosis or undetected lesions. The convolutional neural network is an effective technique for classification in deep learning model. In this study, we utilized median filter, contrast-limited adaptive histogram equalization, and data augmentation to preprocess over 9,000 mammograms, and trained a classified model by using convolutional neural network. The experiment results demonstrated that the accuracy of model with preprocessed images significantly outperformed the model without preprocessed images.
| Original language | English |
|---|---|
| Title of host publication | 2019 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 9-12 |
| Number of pages | 4 |
| ISBN (Electronic) | 9781728108315 |
| DOIs | |
| Publication status | Published - May 2019 |
| Event | 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019 - Chengdu, China Duration: May 25 2019 → May 28 2019 |
Publication series
| Name | 2019 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019 |
|---|
Conference
| Conference | 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019 |
|---|---|
| Country/Territory | China |
| City | Chengdu |
| Period | 5/25/19 → 5/28/19 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- breast cancer detection
- convolution neural network
- deep learning
- mammograms
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems and Management
- Control and Optimization
- Computer Vision and Pattern Recognition
- Artificial Intelligence
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