Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach

Abstract

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.

Detection of Monkeypox Disease from Human Skin Images with a Hybrid Deep Learning Model

“…Thieme et al developed a web-based app for the classification of skin lesions caused by monkeypox virus infection using a large number of monkeypox datasets, and 0.91 sensitivity and 0.898 specificity values were obtained in the test dataset with the pretrained ResNet34 deep learning model [22]. On an open-source monkeypox dataset, Velu et al performed classification with the EfficientNet model and then compared with the reinforcement learning approach Policy Gradient, Actor-Critic, Deep Q-learning network and Double Deep Q-learning network, the highest accuracy was achieved as 0.985 [23]. For the detection of monkeypox disease by Yasmin et al, using DenseNet201, EfficientNetB7, Inception-ResNetV2, InceptionV3, VGG16, and ResNet50 models, the highest accuracy was obtained in the InceptionV3 model, and a fine-tuned version of this model was recommended, and 100% accuracy in the new model called PoxNet22 was achieved [24].…”