College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China.
Front Immunol. 2023 Dec 7;14:1225557. doi: 10.3389/fimmu.2023.1225557. eCollection 2023.
The World Health Organization (WHO) has assessed the global public risk of monkeypox as moderate, and 71 WHO member countries have reported more than 14,000 cases of monkeypox infection. At present, the identification of clinical symptoms of monkeypox mainly depends on traditional medical means, which has the problems of low detection efficiency and high detection cost. The deep learning algorithm is excellent in image recognition and can extract and recognize image features quickly and reliably.
Therefore, this paper proposes a residual convolutional neural network based on the λ function and contextual transformer (LaCTResNet) for the image recognition of monkeypox cases.
The average recognition accuracy of the neural network model is 91.85%, which is 15.82% higher than that of the baseline model ResNet50 and better than the classical convolutional neural networks models such as AlexNet, VGG16, Inception-V3, and EfficientNet-B5.
This method realizes high-precision identification of skin symptoms of the monkeypox virus to provide a fast and reliable auxiliary diagnosis method for monkeypox cases for front-line medical staff.
世界卫生组织（WHO）评估猴痘的全球公共风险为中等，71 个世卫组织成员国报告了超过 14000 例猴痘感染病例。目前，猴痘感染的临床症状主要依靠传统医学手段进行识别，存在检测效率低、检测成本高的问题。深度学习算法在图像识别方面表现出色，能够快速可靠地提取和识别图像特征。
因此，本文提出了一种基于 λ 函数和上下文转换器的剩余卷积神经网络（LaCTResNet），用于猴痘病例的图像识别。
神经网络模型的平均识别准确率为 91.85%，比基线模型 ResNet50 高 15.82%，优于经典卷积神经网络模型，如 AlexNet、VGG16、Inception-V3 和 EfficientNet-B5。
该方法实现了对猴痘病毒皮肤症状的高精度识别，为一线医务人员提供了一种快速可靠的猴痘病例辅助诊断方法。
