Breman JG, Steniowski M, Zanotto E, Gromyko A, Arita I. Human monkeypox, 1970–79. Bull World Health Organ. 1980;58(2):165.
Nolen LD, et al. Extended human-to-human transmission during a monkeypox outbreak in the Democratic Republic of the Congo. Emerg Infect Dis. 2016;22(6):1014.
Outbreak M-CM. Situation update. World Health Organization; 2022. Available online: https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON393. Accessed on 17 June 2022.
Fatima N, Mandava K. Monkeypox—a menacing challenge or an endemic? Ann Med Surg. 2022;79: 103979.
Reed KD, et al. The detection of monkeypox in humans in the Western Hemisphere. N Engl J Med. 2004;350(4):342–50.
Attallah O. MonDiaL-CAD: monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning. Digit Health. 2023;9:20552076231180056.
Liopyris K, Gregoriou S, Dias J, Stratigos AJ. Artificial intelligence in dermatology: challenges and perspectives. Dermatol Therapy. 2022;12(12):2637–51.
Zhou YB. Skin lesion specimens as first choice to detect monkeypox virus. Lancet. 2023;401(10384):1264–5.
Bala D, Hossain MS, Hossain MA, Abdullah MI, Rahman MM, Manavalan B, et al. MonkeyNet: a robust deep convolutional neural network for monkeypox disease detection and classification. Neural Netw. 2023;161:757–775.
Asif S, Zhao M, Tang F, Zhu Y. A deep learning-based framework for detecting COVID-19 patients using chest X-rays. Multimed Syst. 2022;28(4):1495–1513.
Panayides AS, et al. AI in medical imaging informatics: current challenges and future directions. IEEE J Biomed Health Inform. 2020;24(7):1837–57.
Saleh AI, Rabie AH. Human monkeypox diagnose (HMD) strategy based on data mining and artificial intelligence techniques. Comput Biol Med. 2023;152:106383.
Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology. 2020;200905.
Asif S, Zhao M, Li, Y, Tang F, Zhu Y. CFI-Net: a Choquet fuzzy integral based ensemble network with PSO-optimized fuzzy measures for diagnosing multiple skin diseases including Mpox. IEEE J Biomed Health Inform. 2024.
Ahsan MM, Uddin MR, Ali MS, Islam MK, Farjana M, Sakib AN, et al. Deep transfer learning approaches for monkeypox disease diagnosis. Expert Syst Appl. 2023;216:119483.
Asif S, Yi W, Ain QU, Hou J, Yi T, Si J. Improving effectiveness of different deep transfer learning-based models for detecting brain tumors from MR images. IEEE Access. 2022;10:34716–30.
Haque ME, Ahmed MR, Nila RS, Islam S. Human monkeypox disease detection using deep learning and attention mechanisms. In: 2022 25th International Conference on Computer and Information Technology (ICCIT). IEEE; 2022. p. 1069–73.
Polikar R. Ensemble learning. In: Ensemble machine learning: methods and applications. 2012. p. 1–34.
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 7132–41.
Jakubovitz D, Giryes R, Rodrigues MR. Generalization error in deep learning. In: Compressed sensing and its applications. Berlin: Springer; 2019. p. 153–93.
Ahsan MM, Uddin MR, Farjana M, Sakib AN, Momin KA, Luna SA. Image data collection and implementation of deep learning-based model in detecting monkeypox disease using modified VGG16. arXiv preprint http://arxiv.org/abs/2206.01862. 2022.
Sahin VH, Oztel I, Yolcu Oztel G. Human monkeypox classification from skin lesion images with deep pre-trained network using mobile application. J Med Syst. 2022;46(11):79.
Irmak MC, Aydin T, Yağanoğlu M. Monkeypox skin lesion detection with MobileNetV2 and VGGNet models. In: 2022 Medical Technologies Congress (TIPTEKNO). IEEE; 2022. p. 1–4.
Ali SN, et al. Monkeypox skin lesion detection using deep learning models: a feasibility study. arXiv preprint http://arxiv.org/abs/2207.03342. 2022.
Thieme AH, Zheng Y, Machiraju G, Sadee C, Mittermaier M, Gertler M, et al. A deep-learning algorithm to classify skin lesions from mpox virus infection. Nat Med. 2023;29(3):738–747.
Naveen CV, Abhiram G, Aneesh V, Kakulapati V, Kumar K. Monkeypox detection using transfer learning, ResNet50, Alex Net, ResNet18 & custom CNN model. Asian J Adv Res Rep. 2023;17(5):7–13.
Nayak T, Chadaga K, Sampathila N, Mayrose H, Gokulkrishnan N, Prabhu S, et al. Deep learning based detection of monkeypox virus using skin lesion images. Med Novel Technol Devices. 2023;18:100243.
Sharma C, Gulzar Y, Mir MS. A supervised learning-based classification technique for precise identification of monkeypox using skin imaging. Int J Adv Comput Sci Appl. 2025;16(2).
Mehmood A, Gulzar Y, Ilyas QM, Jabbari A, Ahmad M, Iqbal S. SBXception: a shallower and broader xception architecture for efficient classification of skin lesions. Cancers. 2023;15(14):3604.
Sitaula C, Shahi TB. Monkeypox virus detection using pre-trained deep learning-based approaches. J Med Syst. 2022;46(11):78.
Pramanik R, Banerjee B, Efimenko G, Kaplun D, Sarkar R. Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme. PLoS ONE. 2023;18(4): e0281815. https://doi.org/10.1371/journal.pone.0281815.
Uysal F. Detection of monkeypox disease from human skin images with a hybrid deep learning model. Diagnostics. 2023;13(10):1772.
Gulzar Y, et al. Next-generation approach to skin disorder prediction employing hybrid deep transfer learning. Front Big Data. 2025;8:1503883.
Haque ME, Ahmed MR, Nila RS, Islam S. Classification of human monkeypox disease using deep learning models and attention mechanisms. arXiv preprint http://arxiv.org/abs/2211.15459. 2022.
Kundu D, Siddiqi UR, Rahman MM. Vision transformer based deep learning model for monkeypox detection. In: 2022 25th International Conference on Computer and Information Technology (ICCIT). IEEE; 2022. p. 1021–6.
Ahsan MM, et al. Enhancing monkeypox diagnosis and explanation through modified transfer learning, vision transformers, and federated learning. Inform Med Unlock. 2024;45: 101449.
Gulzar Y, Khan SA. Skin lesion segmentation based on vision transformers and convolutional neural networks—a comparative study. Appl Sci. 2022;12(12):5990.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. p. 4700–8.
Howard AG, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint http://arxiv.org/abs/1704.04861. 2017.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. p. 770–8.
Lin M, Chen Q, Yan S. Network in network. arXiv preprint http://arxiv.org/abs/1312.4400. 2013.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(1):1929–58.
Berend D, Paroush J. When is Condorcet’s jury theorem valid? Soc Choice Welf. 1998;15:481–8.
Article MathSciNet Google Scholar
Condorcet MD. Essay on the application of analysis to the probability of majority decisions. Paris: Imprimerie Royale. p. 1785.
Estlund DM. Opinion leaders, independence, and Condorcet’s jury theorem. Theor Decis. 1994;36:131–62.
Austen-Smith D, Banks JS. Information aggregation, rationality, and the Condorcet jury theorem. Am Polit Sci Rev. 1996;90(1):34–45.
Devroye L, Györfi L, Lugosi G. A probabilistic theory of pattern recognition. Berlin: Springer Science & Business Media; 2013.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90.
Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N. A survey on addressing high-class imbalance in big data. J Big Data. 2018;5(1):1–30.
Howard A. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint http://arxiv.org/abs/1704.04861. 2017.
He K, Zhang X, Ren S, Sun J. Identity mappings in deep residual networks. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14. Springer; 2016. p. 630–45.
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision. 2017. p. 618–26.
Dietterich TG. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 1998;10(7):1895–923.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. p. 2818–26.
Szegedy C, Ioffe S, Vanhoucke V, Alemi A. Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 31, no 1. 2017.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C. Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 4510–20.
Zoph B, Vasudevan V, Shlens J, Le QV. Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 8697–710.
Chollet F. Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017. p. 1251–8.
Schwenker F. Ensemble methods: foundations and algorithms [book review]. IEEE Comput Intell Mag. 2013;8(1):77–9.
Article MathSciNet Google Scholar
Wolpert DH. Stacked generalization. Neural Netw. 1992;5(2):241–59.
Aloraini M. An effective human monkeypox classification using vision transformer. Int J Imaging Syst Technol. 2023;34(1):e22944.
Comments (0)