皮肤病变的自动分割是计算机辅助诊断中的关键任务，在皮肤癌的早期检测和治疗中起着至关重要的作用。尽管存在许多基于深度学习的分割方法，但由于分割过程的存在，病变特征的提取仍然不足。因此，皮肤病变图像分割仍然面临着缺失详细信息和对病变区域不准确分割的挑战。在本文中，我们提出了一种用于皮肤病变分割的鬼卷积自适应融合网络。首先，神经网络采用鬼模块而不是普通的卷积层，为全面的目标特征提取生成了丰富的皮肤病变特征图。随后，网络采用自适应融合模块和双边注意力模块连接编码和解码层，促进浅层和深层网络信息的整合。此外，多级输出模式用于像素预测。层特征融合有效地结合了不同尺度的输出特征，从而提高了图像分割的准确性。所提出的网络在三个公开数据集ISIC2016、ISIC2017和ISIC2018上进行了广泛的评估。实验结果显示，准确率分别为96.42%、94.07%和95.03%，Kappa系数分别为90.41%、81.08%和86.96%。我们网络的整体性能超过了现有网络的水平。模拟实验进一步表明，鬼卷积自适应融合网络对皮肤病变图像的分割结果优于其他方法，为皮肤疾病的诊断提供了新的可能性。版权所有 © 2023 Elsevier Ltd.

Automatic segmentation of skin lesions is a pivotal task in computer-aided diagnosis, playing a crucial role in the early detection and treatment of skin cancer. Despite the existence of numerous deep learning-based segmentation methods, the extraction of lesion features remains inadequate as a result of the segmentation process. Consequently, skin lesion image segmentation continues to face challenges regarding missing detailed information and inaccurate segmentation of the lesion region. In this paper, we propose a ghost convolution adaptive fusion network for skin lesion segmentation. First, the neural network incorporates a ghost module instead of the ordinary convolution layer, generating a rich skin lesion feature map for comprehensive target feature extraction. Subsequently, the network employs an adaptive fusion module and bilateral attention module to connect the encoding and decoding layers, facilitating the integration of shallow and deep network information. Moreover, multi-level output patterns are used for pixel prediction. Layer feature fusion effectively combines output features of different scales, thus improving image segmentation accuracy. The proposed network was extensively evaluated on three publicly available datasets: ISIC2016, ISIC2017, and ISIC2018. The experimental results demonstrated accuracies of 96.42%, 94.07%, and 95.03%, and kappa coefficients of 90.41%, 81.08%, and 86.96%, respectively. The overall performance of our network surpassed that of existing networks. Simulation experiments further revealed that the ghost convolution adaptive fusion network exhibited superior segmentation results for skin lesion images, offering new possibilities for the diagnosis of skin diseases.Copyright © 2023 Elsevier Ltd. All rights reserved.