An Enhanced Approach Using AGS Network for Skin Cancer Classification
Skin cancer accounts for over 40% of all cancer diagnoses worldwide. However, accurately diagnosing skin cancer remains challenging for dermatologists, as multiple types of skin cancer often appear visually similar. The diagnostic accuracy of dermatologists ranges between 62% and 80%. Although AI mo...
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MDPI AG
2025-01-01
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| author | Hwanyoung Lee Seeun Cho Jiyoon Song Hoyoung Kim Youjin Shin |
| author_facet | Hwanyoung Lee Seeun Cho Jiyoon Song Hoyoung Kim Youjin Shin |
| author_sort | Hwanyoung Lee |
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| description | Skin cancer accounts for over 40% of all cancer diagnoses worldwide. However, accurately diagnosing skin cancer remains challenging for dermatologists, as multiple types of skin cancer often appear visually similar. The diagnostic accuracy of dermatologists ranges between 62% and 80%. Although AI models have shown promise in assisting with skin cancer classification in various studies, obtaining the large-scale medical image datasets required for AI model training is not straightforward. To address this limitation, this study proposes the AGS network, designed to overcome the challenges of small datasets and enhance the performance of skin cancer classifiers. The AGS network integrates three key modules: Augmentation (A), GAN (G), and Segmentation (S). It was evaluated using eight deep learning classifiers—GoogLeNet, DenseNet201, ResNet50, MobileNet V3, EfficientNet B0, ViT, EfficientNet V2, and Swin Transformers—on the HAM10000 dataset. Five model configurations were also tested to assess the contribution of each module. The results showed that all eight classifiers demonstrated consistent performance improvements with the AGS network. In particular, EfficientNet V2 + AGS achieved the most significant performance gains over the baseline model, with an increase of +0.1808 in Accuracy and +0.1674 in F1-Score. Among all configurations, ResNet50+AGS achieved the best overall performance, with an Accuracy of 95.87% and an F1-Score of 95.73%. While most previous studies focused on single augmentation methods, this study demonstrates the effectiveness of combining multiple augmentation techniques within an integrated framework. The AGS network demonstrates how integrating diverse methods can improve the performance of skin cancer classification models. |
| format | Article |
| id | doaj-art-15b72636d4874544b1bc91446e556ea3 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-15b72636d4874544b1bc91446e556ea32025-01-24T13:48:46ZengMDPI AGSensors1424-82202025-01-0125239410.3390/s25020394An Enhanced Approach Using AGS Network for Skin Cancer ClassificationHwanyoung Lee0Seeun Cho1Jiyoon Song2Hoyoung Kim3Youjin Shin4Department of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon 14662, Republic of KoreaDepartment of Artificial Intelligence, The Catholic University of Korea, Bucheon 14662, Republic of KoreaDepartment of Artificial Intelligence, The Catholic University of Korea, Bucheon 14662, Republic of KoreaDepartment of Computer Science, Stony Brook University, Stony Brook, NY 11794, USADepartment of Data Science, The Catholic University of Korea, Bucheon 14662, Republic of KoreaSkin cancer accounts for over 40% of all cancer diagnoses worldwide. However, accurately diagnosing skin cancer remains challenging for dermatologists, as multiple types of skin cancer often appear visually similar. The diagnostic accuracy of dermatologists ranges between 62% and 80%. Although AI models have shown promise in assisting with skin cancer classification in various studies, obtaining the large-scale medical image datasets required for AI model training is not straightforward. To address this limitation, this study proposes the AGS network, designed to overcome the challenges of small datasets and enhance the performance of skin cancer classifiers. The AGS network integrates three key modules: Augmentation (A), GAN (G), and Segmentation (S). It was evaluated using eight deep learning classifiers—GoogLeNet, DenseNet201, ResNet50, MobileNet V3, EfficientNet B0, ViT, EfficientNet V2, and Swin Transformers—on the HAM10000 dataset. Five model configurations were also tested to assess the contribution of each module. The results showed that all eight classifiers demonstrated consistent performance improvements with the AGS network. In particular, EfficientNet V2 + AGS achieved the most significant performance gains over the baseline model, with an increase of +0.1808 in Accuracy and +0.1674 in F1-Score. Among all configurations, ResNet50+AGS achieved the best overall performance, with an Accuracy of 95.87% and an F1-Score of 95.73%. While most previous studies focused on single augmentation methods, this study demonstrates the effectiveness of combining multiple augmentation techniques within an integrated framework. The AGS network demonstrates how integrating diverse methods can improve the performance of skin cancer classification models.https://www.mdpi.com/1424-8220/25/2/394skin cancer classificationmedical image analysisPGGANUnet |
| spellingShingle | Hwanyoung Lee Seeun Cho Jiyoon Song Hoyoung Kim Youjin Shin An Enhanced Approach Using AGS Network for Skin Cancer Classification Sensors skin cancer classification medical image analysis PGGAN Unet |
| title | An Enhanced Approach Using AGS Network for Skin Cancer Classification |
| title_full | An Enhanced Approach Using AGS Network for Skin Cancer Classification |
| title_fullStr | An Enhanced Approach Using AGS Network for Skin Cancer Classification |
| title_full_unstemmed | An Enhanced Approach Using AGS Network for Skin Cancer Classification |
| title_short | An Enhanced Approach Using AGS Network for Skin Cancer Classification |
| title_sort | enhanced approach using ags network for skin cancer classification |
| topic | skin cancer classification medical image analysis PGGAN Unet |
| url | https://www.mdpi.com/1424-8220/25/2/394 |
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