دوره 22، شماره 2 - ( 9-1404 )
جلد 22 شماره 2 صفحات 55-48 |
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Fateh H, TashnehLab M, Fateh M. Enhancing the Quality of Melanoma Dermatoscopic Images Using Wavelet Coefficients within a Deep Learning Framework. ASWTR 2025; 22 (2) :48-55
URL: http://icml.ir/article-1-675-fa.html
URL: http://icml.ir/article-1-675-fa.html
فاتح هاله. Enhancing the Quality of Melanoma Dermatoscopic Images Using Wavelet Coefficients within a Deep Learning Framework. Advances in Skin, Wound and Tissue Repair. 1404; 22 (2) :48-55
چکیده: (24 مشاهده)
Background: High-quality medical images improve accuracy in diagnosing diseases. Melanoma is a common and deadly skin cancer. Dermatoscopes offer a non-invasive way to capture skin lesions, but dermatoscopic image quality greatly affects diagnosis.
Methods: We propose a deep learning–based method to enhance dermatoscopic image quality using wavelet detail coefficients. ASuper-Resolution Convolutional Neural Network (SRCNN) estimates high-resolution wavelet coefficients from low-resolution images. This approach enables efficient training with fewer samples and lower computational cost. Refinement of these coefficients leads to better image reconstruction, measured using Peak Signal-to-Noise Ratio (PSNR).
Results: Tests on 180 dermatoscopic images show our method achieves a PSNR of 48.99. Previous approaches reached a maximum PSNR of 44.02.
Conclusion: This result shows our framework can provide high-quality images, supporting more accurate melanoma diagnosis.
Methods: We propose a deep learning–based method to enhance dermatoscopic image quality using wavelet detail coefficients. ASuper-Resolution Convolutional Neural Network (SRCNN) estimates high-resolution wavelet coefficients from low-resolution images. This approach enables efficient training with fewer samples and lower computational cost. Refinement of these coefficients leads to better image reconstruction, measured using Peak Signal-to-Noise Ratio (PSNR).
Results: Tests on 180 dermatoscopic images show our method achieves a PSNR of 48.99. Previous approaches reached a maximum PSNR of 44.02.
Conclusion: This result shows our framework can provide high-quality images, supporting more accurate melanoma diagnosis.
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