會議論文
| 學年 | 109 |
|---|---|
| 學期 | 1 |
| 發表日期 | 2021-01-10 |
| 作品名稱 | Data Augmentation for a Deep Learning Framework for Ventricular Septal Defect Ultrasound Image Classification |
| 作品名稱（其他語言） | |
| 著者 | Shih-Hsin Chen; I-Hsin Tai; Yi-Hui Chen; Ken-Pen Weng; Kai-Sheng Hsieh |
| 作品所屬單位 | |
| 出版者 | |
| 會議名稱 | WORKSHOP ON INTEGRATED ARTIFICIAL INTELLIGENCE IN DATA SCIENCE, jointed with ICPR 2020 |
| 會議地點 | Milan, Italy |
| 摘要 | Congenital heart diseases (CHD) can be detected through ultrasound imaging. Although ultrasound can be used for immediate diagnosis, doctors require considerable time to read dynamic clips; typically, physicians must continuously examine disease data from beating heart images. Most importantly, this type of diagnosis relies heavily on the expertise and experience of the diagnosing physician. This study established an ultrasound image classification with deep learning algorithms to overcome the challenges involved in CHD diagnosis. We detected the most common CHD, namely the first, second, and fourth types of ventricular septal defect (VSD). We improved the performance levels of well-known deep learning algorithms (InceptionV3, ResNet, and DenseNet). Because algorithm optimization and overfitting problems can influence the performance of deep learning algorithms, we studied some optimizer algorithms and early-stopping strategies. To enhance the solution quality, we used data augmentation methods for solving this classification problem. The selected approach was further compared with Google AutoML, which applies structure search for quality prediction. Our results revealed that the proposed deep learning algorithm was able to recognize most types of VSD. However, one type of VSD remains unconquered and warrants more advanced techniques. |
| 關鍵字 | Ventricular septal defect (VSD);Echo;Deep learning;Classification;Data augmentation |
| 語言 | en |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20210110~20210115 |
| 通訊作者 | |
| 國別 | ITA |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | Pattern Recognition. ICPR International Workshops and Challenges, p.310-322 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/121525 ) |