Skin Lesion Segmentation for Melanoma Using Dilated DenseUNet

Skin Lesion Segmentation for Melanoma Using Dilated DenseUNet

DenseUNet;melanoma;segmentation;skin cancer;skin lesion

Melanoma, a highly malignant form of skin cancer, affects individuals of all genders and is associated with high mortality rates, especially in advanced stages. The use of tele-dermatology has emerged as a proficient diagnostic approach for skin lesionsandisparticularly beneficial in rural areas with limited access to dermatologists. However, accurately, and efficiently segmenting melanoma remains a challenging task due to the significant diversity observed in the morphology, pigmentation, and dimensions of cutaneous nevi. To address this challenge, we propose a novel approach called DenseUNet-169 with a dilatedconvolution encoder-decoder for automatic segmentation of RGB dermascopic images. By incorporating dilated convolution, our model improves the receptive field of the kernels without increasing the number of parameters. Additionally, we used a method called Copy and Concatenation Attention Block (CCAB) for robust feature computation. To evaluate the performance of our proposed framework, we utilizedthe International Skin Imaging Collaboration (ISIC) 2017 dataset. The experimental results demonstrate the reliability and effectiveness of our suggested approach compared to existing methodologies. Our framework achieveda high level of accuracy (98.38%), precision (96.07%), recall (94.32%), dice score (95.07%), and Jaccardscore(90.45%), outperforming current techniques.

Ammar S. Al-Zubaidi1,*, Mohammed Al-Mukhtar1,Mina H. Al-hashimi2& HarisIjaz3

https://journals.itb.ac.id/index.php/jictra/article/view/21683/6659

Al-Mansour University College

2024

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English

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