Abdelrahim Elsaid Md, Hashim Hasan, Atlam El-Sayed, Osman Radwa Ahmed, Gad Ibrahim
Computer Science Department, Science College, Northern Border University (NBU), Arar 73213, Saudi Arabia.
Computer Science Department, Faculty of Science, Tanta University, Tanta 31527, Egypt.
Diagnostics (Basel). 2024 Nov 23;14(23):2638. doi: 10.3390/diagnostics14232638.
BACKGROUND/OBJECTIVES: The emergence of monkeypox outside its endemic region in Africa has raised significant concerns within the public health community due to its rapid global dissemination. Early clinical differentiation of monkeypox from similar diseases, such as chickenpox and measles, presents a challenge. The Monkeypox Skin Lesion Dataset (MSLD) used in this study comprises monkeypox skin lesions, which were collected primarily from publicly accessible sources. The dataset contains 770 original images captured from 162 unique patients. The MSLD includes four distinct class labels: monkeypox, measles, chickenpox, and normal.
This paper presents an ensemble model for classifying the monkeypox dataset, which includes transformer models and support vector machine (SVM). The model development process begins with an evaluation of seven convolutional neural network (CNN) architectures. The proposed model is developed by selecting the top four models based on evaluation metrics for performance. The top four CNN architectures, namely EfficientNetB0, ResNet50, MobileNet, and Xception, are used for feature extraction. The high-dimensional feature vectors extracted from each network are then concatenated and optimized before being inputted into the SVM classifier.
The proposed ensemble model, in conjunction with the SVM classifier, achieves an accuracy of 95.45b%. Furthermore, the model demonstrates high precision (95.51%), recall (95.45%), and F1 score (95.46%), indicating its effectiveness in identifying monkeypox lesions.
The results of the study show that the proposed hybrid framework achieves robust diagnostic performance in monkeypox detection, offering potential utility for enhanced disease monitoring and outbreak management. The model's high diagnostic accuracy and computational efficiency indicate that it can be used as an additional tool for clinical decision support.
背景/目的：猴痘在非洲流行地区以外出现，因其在全球迅速传播，引起了公共卫生界的高度关注。猴痘与水痘和麻疹等相似疾病的早期临床鉴别是一项挑战。本研究使用的猴痘皮肤病变数据集（MSLD）包含主要从公开可获取来源收集的猴痘皮肤病变。该数据集包含从162名独特患者处捕获的770张原始图像。MSLD包括四个不同的类别标签：猴痘、麻疹、水痘和正常。
本文提出了一种用于对猴痘数据集进行分类的集成模型，其中包括Transformer模型和支持向量机（SVM）。模型开发过程始于对七种卷积神经网络（CNN）架构的评估。通过根据性能评估指标选择前四个模型来开发所提出的模型。前四个CNN架构，即EfficientNetB0、ResNet50、MobileNet和Xception，用于特征提取。然后将从每个网络提取的高维特征向量连接起来并进行优化，再输入到SVM分类器中。
所提出的集成模型与SVM分类器相结合，准确率达到95.45%。此外，该模型还表现出高精度（95.51%）、召回率（95.45%）和F1分数（95.46%），表明其在识别猴痘病变方面的有效性。
研究结果表明，所提出的混合框架在猴痘检测中实现了强大的诊断性能，为加强疾病监测和疫情管理提供了潜在的实用价值。该模型的高诊断准确性和计算效率表明，它可用作临床决策支持的额外工具。
