This research presents DNLR‐NET, a novel model designed for automated and accurate diagnosis of MPox disease. The model's performance is constructed and validated using a carefully collected MPox dataset from online repositories. DNLR‐NET begins by extracting deep features from the DenseNet201 pre‐trained model, which exhibited superior performance compared to other models during the comparison. The deep features obtained from each dense layer are then used to train six classifiers, among which logistic regression showcases the best performance with the extracted deep, dense feature. A comparative study with earlier advanced CNN models classifying the same dataset demonstrates that DNLR‐NET achieves an impressive accuracy of 97.55%, outperforming the base DenseNet201 model, which only attains 95.91% accuracy. This accuracy emphasizes the efficacy of combining deep features with logistic regression. A Grid Search algorithm is employed for optimal hyperparameter extraction, creating multiple unified deep feature sets and achieving the highest classification accuracy. The fusion of deep features with logistic regression yields superior results compared to ensemble techniques such as random forest and support vector machines and also reduces training time complexity. DNLR‐NET surpasses existing models, ML classifiers, and pre‐trained models, demonstrating its effectiveness and potential for clinical implementation in diagnosing MPox. The promising outcomes of advantage deep learning algorithms, particularly DenseNet201 transfer learning, highlight the significance of adopting transfer learning methodologies for CNN‐based MPox diagnosis in clinical settings. Researchers and clinicians are strongly encouraged to explore and implement these techniques to improve the accuracy and efficiency of MPox diagnosis.

中文翻译：

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从皮肤病变图像诊断 Mpox 疾病的深度混合模型

这项研究提出了 DNLR-NET，这是一种专为自动、准确诊断 MPox 疾病而设计的新型模型。该模型的性能是使用从在线存储库仔细收集的 MPox 数据集构建和验证的。DNLR-NET 首先从 DenseNet201 预训练模型中提取深层特征，在比较过程中与其他模型相比，该模型表现出了优越的性能。然后，使用从每个密集层获得的深层特征来训练六个分类器，其中逻辑回归展示了提取的深层密集特征的最佳性能。与对相同数据集进行分类的早期高级 CNN 模型进行的比较研究表明，DNLR-NET 的准确率高达 97.55%，优于仅达到 95.91% 准确率的基本 DenseNet201 模型。这种准确性强调了将深度特征与逻辑回归相结合的功效。采用网格搜索算法进行最优超参数提取，创建多个统一的深度特征集，实现最高的分类精度。与随机森林和支持向量机等集成技术相比，深度特征与逻辑回归的融合产生了更好的结果，并且还降低了训练时间复杂度。DNLR-NET 超越了现有模型、ML 分类器和预训练模型，证明了其在诊断 MPox 方面的有效性和临床实施潜力。优势深度学习算法（尤其是 DenseNet201 迁移学习）的可喜成果凸显了在临床环境中采用迁移学习方法进行基于 CNN 的 MPox 诊断的重要性。强烈鼓励研究人员和临床医生探索和实施这些技术，以提高 MPox 诊断的准确性和效率。