School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, 341000, China; Jinguan Copper Branch of Tongling Nonferrous Metals Group Co, Ltd, Tongling, Anhui, 244100, China.
School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, Jiangxi, 341000, China.
Comput Biol Med. 2023 Sep;164:107273. doi: 10.1016/j.compbiomed.2023.107273. Epub 2023 Jul 27.
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.
皮肤病变的自动分割是计算机辅助诊断中的一个关键任务，对于皮肤癌的早期检测和治疗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，但由于分割过程的原因，病变特征的提取仍然不够充分。因此，皮肤病变图像分割仍然面临着缺失详细信息和病变区域分割不准确的挑战。在本文中，我们提出了一种用于皮肤病变分割的幽灵卷积自适应融合网络。首先，神经网络采用幽灵模块代替普通卷积层，生成丰富的皮肤病变特征图，进行全面的目标特征提取。然后，网络采用自适应融合模块和双边注意模块连接编码和解码层，促进浅层和深层网络信息的融合。此外，还使用了多层次的输出模式进行像素预测。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。该网络在三个公开可用的数据集上进行了广泛评估：ISIC2016、ISIC2017 和 ISIC2018。实验结果表明，该网络的准确率分别为 96.42%、94.07%和 95.03%，kappa 系数分别为 90.41%、81.08%和 86.96%。与现有的网络相比，我们的网络整体性能更好。仿真实验进一步表明，幽灵卷积自适应融合网络对皮肤病变图像具有优越的分割效果，为皮肤疾病的诊断提供了新的可能性。
