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Transforming Clinical and Dermoscopic Images Using CycleGAN : Bridging Data Scarcity for AI-Driven Dermatology

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  • 발행기관
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  • 간행물
    선문효정학술연구회 학술대회 프로시딩 바로가기
  • 통권
    Proceedings of THE 5th INTERNATIONAL CONFERENCE OF HYOJEONG ACADEMY (2025.02)바로가기
  • 페이지
    pp.61-65
  • 저자
    Sarreha Tasmin Rikta, Wonsang You
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A489344

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초록

영어
Dermoscopy and clinical imaging are crucial for diagnosing skin conditions, as they offer complementary insights that deepen our understanding of the underlying issues. While clinical images can be relatively straightforward to obtain, capturing dermoscopic images often presents more difficulties due to practical constraints, budgetary issues, and the requirement for specialized equipment. This limited availability of dermoscopic data creates significant obstacles for developing effective deep learning models in dermatology. To tackle this challenge, we introduce an innovative approach utilizing CycleGAN (Cycle Generative Adversarial Network), a deep learning model tailored for bidirectional image-to-image translation, to create synthetic dermoscopic and clinical images from their respective counterparts. Our method converts clinical images into dermoscopic images and vice versa, creating a synthetic dataset that preserves the essential pathological features. The qualitative results show that CycleGAN successfully generates realistic synthetic images that are consistent with the underlying pathology, effectively bridging the gap between the two modalities. Our approach aims to enhance diagnostic accuracy and lessen the dependence on costly data collection methods. This study establishes a foundation for utilizing synthetic data in medical imaging, tackling the challenges posed by limited dermoscopic datasets, and promoting the integration of artificial intelligence into clinical practice.

목차

Abstract
1. Introduction
2. Materials and Methods
2.1. Dataset Preprocessing
2.2. Overview of CycleGAN architecture
3. Results and Discussion
5. Conclusions and future work
References

키워드

Dermoscopic Clinical Deep Learning CycleGAN Synthetic data

저자

  • Sarreha Tasmin Rikta [ Dept. of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ]
  • Wonsang You [ Dept. of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    선문효정학술연구회 [Sun Moon Hyojeong Academy Society]
  • 설립연도
    2023
  • 분야
    복합학>학제간연구
  • 소개
    Journal of Hyojeong Academia aims to serve as a global platform where researchers and scholars of various disciplines can contribute ideas for our sustainable global community of Co‐existence, Co‐prosperity, and Co‐righteousness. The journal is a multidisciplinary, open‐access, internationally peer‐reviewed academic journal, and it invites all areas of research conducted in the spirit of post materialism including studies centering on God, studies unifying religions and sciences, and studies on all aspects of Co‐existence, Co‐prosperity, and Co‐righteousness.

간행물

  • 간행물명
    선문효정학술연구회 학술대회 프로시딩
  • 간기
    반년간
  • 수록기간
    2023~2026
  • 십진분류
    KDC 238 DDC 289

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