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Improving acne severity detection: a GAN framework with contour accentuation for image deblurring.
Philomina Princiya Mascarenhas1, M S Sannidhan1, Ancilla J Pinto2
1Department of Computer Science and Engineering, NMAM Institute of Technology (Nitte Deemed to be University), Nitte, India.
Frontiers in Bioinformatics
|March 25, 2025
Summary
This study introduces a new generative network framework to improve teledermatology by deblurring acne images. The method enhances image clarity for more accurate acne diagnosis in young adults.
Area of Science:
- Dermatology
- Computer Vision
- Medical Imaging
Background:
- Teledermatology is crucial for diagnosing skin conditions like acne, particularly in young adults.
- Image blurring in teledermatology hinders accurate diagnosis by obscuring crucial details.
- Existing deblurring methods are inadequate for recovering fine details essential for teledermatology.
Purpose of the Study:
- To develop an advanced framework for deblurring images in teledermatology, specifically for acne diagnosis.
- To enhance the clarity and detail of blurred facial images for improved diagnostic accuracy.
- To overcome limitations of traditional deblurring techniques in the context of teledermatology.
Main Methods:
- A novel framework utilizing generative networks is proposed.
- The framework incorporates a Contour Accentuation Technique to outline facial features.
- It includes a deblurring module for sketch enhancement and an image translator for color photo generation.
Main Results:
- The framework achieved a Structural Similarity Index (SSIM) of 0.83.
- Peak Signal-to-Noise Ratio (PSNR) reached 22.35 dB.
- The Fréchet Inception Distance (FID) score was 10.77, indicating high-quality image generation.
Conclusions:
- The proposed framework effectively produces clear images for accurate acne diagnosis via teledermatology.
- Generative networks offer a promising solution for deblurring challenges in medical imaging.
- The results demonstrate significant improvements in image quality for teledermatology applications.
