| Title |
A New Image Augmentation Method for Improving Computer-aided Diagnosis System Performance |
| Authors |
이신애(Sin-ae Lee) ; 조현종(Hyun-chong Cho) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.1.102 |
| Keywords |
Augmentation; Computer-aided diagnosis(CADx); Deep learning; Gastric endoscopy |
| Abstract |
Gastric cancer is the largest percentage of cancer cases in Korea. A precise way to find the occurrence of gastrointestinal diseases is through gastroscopy by a trained diagnostic physician. Computer-aided diagnosis (CADx) system helps improve the reliability and speed of diagnosis. The CADx system has developed with deep learning, which is data dependent. However, medical image data is labor intensive and time consuming, making it difficult for large data sets to be formed. To solve this problem, it is important to apply augmentation techniques. In this paper, we propose an augmentation method which is suitable for the data. A basic classification model was made by leaning a data set consisting of the original images. Each of the 14 augment technique-applied data set was input into the generated model and the f1-score values were compared. The f1-score of the highest performance among the proposed methods, was 0.9221, with an increase of about 0.085. |