AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation

• URL: http://arxiv.org/abs/2409.05420v1
• Date: Mon, 9 Sep 2024 08:21:17 GMT
• Title: AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation
• Authors: Asim Naveed, Syed S. Naqvi, Tariq M. Khan, Shahzaib Iqbal, M. Yaqoob Wani, Haroon Ahmed Khan,
• Abstract summary: This research presents a robust approach utilizing a dilated convolutional residual network. It incorporates an attention-based spatial feature enhancement block (ASFEB) and employs a guided decoder strategy. The effectiveness of the proposed AD-Net was evaluated using four public benchmark datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In computer-aided diagnosis tools employed for skin cancer treatment and early diagnosis, skin lesion segmentation is important. However, achieving precise segmentation is challenging due to inherent variations in appearance, contrast, texture, and blurry lesion boundaries. This research presents a robust approach utilizing a dilated convolutional residual network, which incorporates an attention-based spatial feature enhancement block (ASFEB) and employs a guided decoder strategy. In each dilated convolutional residual block, dilated convolution is employed to broaden the receptive field with varying dilation rates. To improve the spatial feature information of the encoder, we employed an attention-based spatial feature enhancement block in the skip connections. The ASFEB in our proposed method combines feature maps obtained from average and maximum-pooling operations. These combined features are then weighted using the active outcome of global average pooling and convolution operations. Additionally, we have incorporated a guided decoder strategy, where each decoder block is optimized using an individual loss function to enhance the feature learning process in the proposed AD-Net. The proposed AD-Net presents a significant benefit by necessitating fewer model parameters compared to its peer methods. This reduction in parameters directly impacts the number of labeled data required for training, facilitating faster convergence during the training process. The effectiveness of the proposed AD-Net was evaluated using four public benchmark datasets. We conducted a Wilcoxon signed-rank test to verify the efficiency of the AD-Net. The outcomes suggest that our method surpasses other cutting-edge methods in performance, even without the implementation of data augmentation strategies.

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