| Title | Ocular Disease Recognition and Classification using TripleGAN |
| Creator | |
| Date Issued | 2023-07-21 |
| Source Publication | ACM International Conference Proceeding Series
 |
| Pages | 7-11 |
| Abstract | Ocular diseases pose significant challenges in the field of ophthalmology. The accurate classification of fundus images depicting various ocular diseases plays a vital role in early diagnosis and effective treatment planning. However, medical datasets for such conditions often suffer from limited samples and imbalanced class distributions, making classification tasks more challenging. In this study, we address the multi-classification of fundus images of ocular diseases, considering the aforementioned dataset limitations. We specifically focus on three distinct ocular diseases, along with normal fundus images. In medical data analysis, the availability of labeled data is often limited, and the distribution of classes is frequently imbalanced. To address these challenges, we propose the utilization of triple generative adversarial nets (TripleGAN), a semi-supervised generative network, for medical data classification tasks. Through rigorous experimentation and training, we demonstrate the effectiveness and robustness triple generative adversarial nets. After 500 iterations, our model achieves a stable test accuracy of approximately 70%. Furthermore, our approach exhibits resilience to loss variation, ensuring consistent performance across different stages of training. The results of our study indicate promising potential in the accurate classification of fundus images of ocular diseases, even in the presence of under-sampled and unbalanced datasets. Our findings contribute to the research in the field of ophthalmic image analysis and may aid in the development of advanced diagnostic tools for ocular diseases. |
| Keyword | Convolutional Neural Networks Image Recognition Ocular Disease Diagnosis Triple Generative Adversarial Nets |
| DOI | 10.1145/3613307.3613309 |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85175971826 |
| Citation statistics | |
| Document Type | Conference paper |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/11549 |
| Collection | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author | Gong，Xi |
| Affiliation | Department of Statistics and Data Science,BNU-HKBU United International College,Zhuhai,519087,China |
| First Author Affilication | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author Affilication | Beijing Normal-Hong Kong Baptist University |
| Recommended Citation GB/T 7714 | Fan，Mingxuan,Peng，Xiaoling,Gong，Xi. Ocular Disease Recognition and Classification using TripleGAN[C], 2023: 7-11. |
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