| Title |
A Study on the Computer Aided Diagnosis System for Early Gastric Cancer Lesion Based on EfficientNetV2-L through Data Filtering |
| Authors |
이한성(Han-sung Lee) ; 조현종(Hyun-chong Cho) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.9.1259 |
| Keywords |
Computer-aided diagnosis(CADx); Deep learning; Early gastric cancer; Image augmentation |
| Abstract |
Gastric cancer is a common cancer worldwide, especially in Korea. Early diagnosis is very important to increase the full recovery rate. However, early gastric cancer has no special symptoms and is a disease that even experts find difficult to diagnose in gastroscopy. Therefore, in this paper proposed a computer-aided diagnosis(CADx) for early gastric cancer diagnosis using EfficientNetV2-L. Due to the nature of medical data, it is difficult to collect a large amount of data. The data used for training was augmented using Cifar10 policy of the Google's AutoAugment. Additionally, the augmented image was used as an input to the model trained with the original dataset and filtered according to the classification threshold. EfficientNetV2 is a classification network designed Training-NAS that can learn the feature of lesions with a small number of parameters. As a result, EfficientNetV2 set to the threshold value of 0.9 achieved the performance of accuracy 0.943 for early gastric cancer and abnormal image classification. The AUC value also increases from 0.972 to 0.991, showing that the data filtering method of this study was effective for improvement of classification performance. |