UTILIZING COMPUTER VISION FOR SKIN HEALTH MANAGEMENT IN ATHLETES: ENHANCEMENTS IN DERMATOLOGICAL DIAGNOSIS AND TREATMENT

Abstract

Objective: To develop a computer vision-based dermatology auxiliary diagnosis model optimized for athletic skin conditions, addressing the challenges of effectively capturing the chronological progression of dermatological issues, reducing labor costs in medical feature construction, and managing limited dermatological data samples. Methods: The model begins with a pre-training phase using a Generative Adversarial Network (GAN) to create virtual dermatological images, simulating common skin conditions in athletes such as abrasions, infections, and sun damage. Subsequently, the model employs transfer learning techniques to fine-tune the plain Bayesian network with multiple real dermatological datasets tailored to athletic populations. Results: The application of this model on seven real dermatological datasets, which include conditions prevalent in athletes, demonstrated superior classification accuracy and recall in six out of the seven datasets. This indicates the model's robust capability to adapt to the unique dermatological needs of athletes. Conclusion: This innovative computer vision approach offers a promising tool for sports medicine practitioners, enhancing their ability to diagnose and manage skin conditions in athletes more effectively. By leveraging advanced imaging technologies and machine learning, the model not only improves diagnostic accuracy but also offers a scalable solution that can adapt to various sports-related dermatological conditions. This could lead to more personalized and timely treatments, ultimately helping athletes maintain skin health and overall performance.