Velu Malathi, Dhanaraj Rajesh Kumar, Balusamy Balamurugan, Kadry Seifedine, Yu Yang, Nadeem Ahmed, Rauf Hafiz Tayyab
School of Computer Science and Engineering, Panimalar Engineering College, Poonamallee, Chennai 600123, India.
School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India.
Diagnostics (Basel). 2023 Apr 20;13(8):1491. doi: 10.3390/diagnostics13081491.
While the world is working quietly to repair the damage caused by COVID-19's widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypox disease may be detected using artificial intelligence techniques. This paper suggests two strategies for improving monkeypox image classification precision. Based on reinforcement learning and parameter optimization for multi-layer neural networks, the suggested approaches are based on feature extraction and classification: the Q-learning algorithm determines the rate at which an act occurs in a particular state; Malneural networks are binary hybrid algorithms that improve the parameters of neural networks. The algorithms are evaluated using an openly available dataset. In order to analyze the proposed optimization feature selection for monkeypox classification, interpretation criteria were utilized. In order to evaluate the efficiency, significance, and robustness of the suggested algorithms, a series of numerical tests were conducted. There were 95% precision, 95% recall, and 96% f1 scores for monkeypox disease. As compared to traditional learning methods, this method has a higher accuracy value. The overall macro average was around 0.95, and the overall weighted average was around 0.96. When compared to the benchmark algorithms, DDQN, Policy Gradient, and Actor-Critic, the Malneural network had the highest accuracy (around 0.985). In comparison with traditional methods, the proposed methods were found to be more effective. Clinicians can use this proposal to treat monkeypox patients and administration agencies can use it to observe the origin and current status of the disease.
当世界正悄然努力修复新冠病毒广泛传播所造成的损害时，猴痘病毒却有成为全球大流行疾病的威胁。尽管该病毒的致死率和传染性低于新冠病毒，但仍有几个国家每天都报告有新的猴痘病例。猴痘疾病可以使用人工智能技术进行检测。本文提出了两种提高猴痘图像分类精度的策略。基于强化学习和多层神经网络的参数优化，所提出的方法基于特征提取和分类：Q学习算法确定在特定状态下某一行为发生的速率；Malneural网络是一种二元混合算法，用于优化神经网络的参数。使用一个公开可用的数据集对这些算法进行评估。为了分析所提出的用于猴痘分类的优化特征选择，采用了解释标准。为了评估所提出算法的效率、显著性和稳健性，进行了一系列数值测试。猴痘疾病的精确率为95%，召回率为95%，F1分数为96%。与传统学习方法相比，该方法具有更高的准确率值。总体宏观平均值约为0.95，总体加权平均值约为0.96。与基准算法DDQN、策略梯度和演员评论家相比，Malneural网络的准确率最高（约为0.985）。与传统方法相比，所提出的方法更有效。临床医生可以利用这一建议来治疗猴痘患者，管理机构可以利用它来观察该疾病的起源和现状。
