JuryFusionNet: a Condorcet’s jury theorem-based CNN ensemble for enhanced monkeypox detection from skin lesion images

Breman JG, Steniowski M, Zanotto E, Gromyko A, Arita I. Human monkeypox, 1970–79. Bull World Health Organ. 1980;58(2):165.

Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

Reed KD, et al. The detection of monkeypox in humans in the Western Hemisphere. N Engl J Med. 2004;350(4):342–50.

Article  Google Scholar 

Attallah O. MonDiaL-CAD: monkeypox diagnosis via selected hybrid CNNs unified with feature selection and ensemble learning. Digit Health. 2023;9:20552076231180056.

Article  Google Scholar 

Liopyris K, Gregoriou S, Dias J, Stratigos AJ. Artificial intelligence in dermatology: challenges and perspectives. Dermatol Therapy. 2022;12(12):2637–51.

Article  Google Scholar 

Zhou YB. Skin lesion specimens as first choice to detect monkeypox virus. Lancet. 2023;401(10384):1264–5.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

Panayides AS, et al. AI in medical imaging informatics: current challenges and future directions. IEEE J Biomed Health Inform. 2020;24(7):1837–57.

Article  Google Scholar 

Saleh AI, Rabie AH. Human monkeypox diagnose (HMD) strategy based on data mining and artificial intelligence techniques. Comput Biol Med. 2023;152:106383.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Chapter  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

Sitaula C, Shahi TB. Monkeypox virus detection using pre-trained deep learning-based approaches. J Med Syst. 2022;46(11):78.

Article  Google Scholar 

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.

Article  Google Scholar 

Uysal F. Detection of monkeypox disease from human skin images with a hybrid deep learning model. Diagnostics. 2023;13(10):1772.

Article  Google Scholar 

Gulzar Y, et al. Next-generation approach to skin disorder prediction employing hybrid deep transfer learning. Front Big Data. 2025;8:1503883.

Article  Google Scholar 

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.

Article  Google Scholar 

Gulzar Y, Khan SA. Skin lesion segmentation based on vision transformers and convolutional neural networks—a comparative study. Appl Sci. 2022;12(12):5990.

Article  Google Scholar 

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.

MathSciNet  Google Scholar 

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.

Article  Google Scholar 

Austen-Smith D, Banks JS. Information aggregation, rationality, and the Condorcet jury theorem. Am Polit Sci Rev. 1996;90(1):34–45.

Article  Google Scholar 

Devroye L, Györfi L, Lugosi G. A probabilistic theory of pattern recognition. Berlin: Springer Science & Business Media; 2013.

Google Scholar 

Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

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.

Article  Google Scholar 

Aloraini M. An effective human monkeypox classification using vision transformer. Int J Imaging Syst Technol. 2023;34(1):e22944.

Article  Google Scholar 

Comments (0)

No login
gif