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All Journal International Journal of Advances in Intelligent Informatics
Al-Fahsi, Resha Dwika Hefni
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Computer Science & IT

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Enhanced U-Net architecture with CNN backbone for accurate segmentation of skin lesions in dermoscopic images Aqthobirrobbany, Aqil; Al-Fahsi, Resha Dwika Hefni; Soesanti, Indah; Nugroho, Hanung Adi
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.1379

Abstract

Addressing the critical public health challenge of skin cancer, particularly melanoma and non-melanoma, this study focuses on enhancing early diagnosis through improved automatic segmentation of skin lesions in dermoscopic images. The researchers propose an optimized U-Net architecture that integrates advanced convolutional neural networks (CNNs) with backbone models such as ResNet50, VGG16, and MobileNetV2, specifically designed to handle the inherent variability and artifacts in dermoscopic imagery. The method's effectiveness was validated using the ISIC-2018 dataset, and our U-Net model incorporating the VGG16 backbone achieved notable improvements in segmentation accuracy, demonstrating an accuracy rate of 0.93. These results signify significant enhancements over existing methods, emphasizing the potential of the proposed approach in aiding precise skin cancer diagnosis and detection. This study makes a valuable contribution to dermatological imaging by presenting an advanced method that substantially boosts the accuracy of skin lesion segmentation, addressing a crucial need in public health.
Co-Authors Aqthobirrobbany, Aqil Hanung Adi Nugroho Indah Soesanti