Mohammed, S. S. & Al-Tuwaijari, J. M. Skin disease classification system based mostly on machine learning approach: a Survey. IOP Conf. Ser. Mater. Sci. Eng. 1076 (012045), 1–13 (2021).
Al-Tbali, J., Anam, L., Al-Jamrah, Okay. M. & Abdul Moaen, F. Chickenpox Outbreak Investigation in Assabain District, Sana’a Metropolis, Yemen, January to February 2019, Iproceedings, vol. 8, no. 8, pp. 1–2, doi: (2022). https://doi.org/10.2196/36598
Sanjita, S., Azeem, M. & Islamovna, U. G. Survey and outbreak of hen pox; acknowledgement by med-student, in Proceedings of the 2nd Worldwide Scientific and Sensible Convention, Brussels, Belgium, pp. 77–82. (2023).
Nasiba, P. & Dildora, B. CHICKENPOX, in Proceedings of Worldwide Convention on Scientific Analysis in Pure and Social Sciences, Toronto, Canada, pp. 202–205. (2023).
Kujur, A., Kiran, Okay. A. & Kujur, M. An Epidemiological Research of Outbreak Investigation of Chickenpox in distant hamlets of a tribal state in India. Cureus 14 (6), 1–11. https://doi.org/10.7759/cureus.26454 (2022).
Verma, R., Bairwa, M., Chawla, S., Prinja, S. & Rajput, M. Ought to Chickenpox vaccine be included within the nationwide immunization schedule in India? Hum. Vaccin. 7 (8), 874–877. https://doi.org/10.4161/hv.7.8.15685 (2011).
Chovatiya, R. & Silverberg, J. I. Inpatient morbidity and mortality of measles in the US. PLOS ONE. 15, 1–13. https://doi.org/10.1371/journal.pone.0231329 (2020). no. 4.
Rabaan, A. A. et al. Updates on measles incidence and eradication: emphasis on the immunological points of Measles an infection. Medicina 58, 1–20. https://doi.org/10.3390/medicina58050680 (2022). no. 5.
Homosexual, N. J. The idea of Measles Elimination: implications for the design of elimination methods. J. Infect. Dis. 189, 27–35. https://doi.org/10.1086/381592 (2004).
Thornhill, J. P. et al. Monkeypox Virus an infection in people throughout 16 international locations – April-June 2022. N Engl. J. Med. 387 (8), 679–691. https://doi.org/10.1056/NEJMoa2207323 (2022).
Mitjà, O. et al. Monkeypox, Lancet, vol. 401, no. 10370, pp. 60–74, doi: (2023). https://doi.org/10.1016/S0140-6736(22)02075-X
Shchelkunov, S. N. et al. Evaluation of the monkeypox virus genome. Virology 297 (2), 172–194. https://doi.org/10.1006/viro.2002.1446 (2002).
Nguyen, P. Y., Ajisegiri, W., Costantino, V., Chughtai, A. A. & MacIntyre, C. R. Reemergence of human monkeypox and declining Inhabitants Immunity within the context of urbanization, Nigeria, 2017–2020. Emerg. Infect. Dis. 27 (4), 1007–1014 (2021).
Doucleff, M. The unfold of monkeypox was predicted by scientists in 1988: Goats and Soda : NPR. Accessed: Aug. 28, 2022. [Online]. Accessible: https://www.npr.org/sections/goatsandsoda/2022/05/27/1101751627/scientists-warned-us-about-monkeypox-in-1988-heres-why-they-were-right
Multi-country monkeypox outbreak in non-endemic international locations. Accessed: Aug. 28. [Online]. Accessible: (2022). https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON385
Bunge, E. M. et al. The altering epidemiology of human monkeypox—A possible risk? A scientific assessment. PLoS Negl. Trop. Dis. 16, 1–20. https://doi.org/10.1371/journal.pntd.0010141 (2022). no. 2.
Mansour, R. F., Althubiti, S. A. & Alenezi, F. Pc Imaginative and prescient with Machine Learning enabled skin lesion classification mannequin. Comput. Mater. Contin. 73 (1), 849–864. https://doi.org/10.32604/cmc.2022.029265 (2022).
Dong, S., Wang, P. & Abbas, Okay. A survey on deep learning and its purposes. Comput. Sci. Rev. 40, 1–22. https://doi.org/10.1016/j.cosrev.2021.100379 (2021).
Abdullah, A. A., Hassan, M. M. & Mustafa, Y. T. A assessment on bayesian deep learning in Healthcare: Functions and challenges. IEEE Entry. 10, 36538–36562. https://doi.org/10.1109/ACCESS.2022.3163384 (2022).
Yan, Okay., Wang, X., Lu, L. & Summers, R. M. DeepLesion: automated mining of large-scale lesion annotations and common lesion detection with deep learning. J. Med. Imaging. 5 (03), 1–11. https://doi.org/10.1117/1.jmi.5.3.036501 (2018).
Kijowski, R., Liu, F., Caliva, F. & Pedoia, V. Deep learning for Lesion Detection, Development, and prediction of Musculoskeletal Disease. J. Magn. Reson. Imaging. 52 (6), 1607–1619. https://doi.org/10.1002/jmri.27001 (2020).
Anupama, C. S. S. et al. Deep learning with backtracking search optimization based mostly skin lesion analysis mannequin. Comput. Mater. Contin. 70 (1), 1297–1313. https://doi.org/10.32604/cmc.2022.018396 (2021).
Talo, M., Baloglu, U. B., Yıldırım, Ö. & Rajendra Acharya, U. Utility of deep transfer learning for automated mind abnormality classification using MR pictures. Cogn. Syst. Res. 54, 176–188. https://doi.org/10.1016/j.cogsys.2018.12.007 (2019).
Ozturk, T. et al. Automated detection of COVID-19 instances using deep neural networks with X-ray pictures. Comput. Biol. Med. 121, 1–11. https://doi.org/10.1016/j.compbiomed.2020.103792 (2020).
Kott, O. et al. Growth of a deep learning algorithm for the histopathologic analysis and Gleason grading of prostate Most cancers biopsies: a pilot examine. Eur. Urol. Focus. 7 (2), 347–351. https://doi.org/10.1016/j.euf.2019.11.003 (2021).
Shkolyar, E. et al. Augmented bladder tumor detection using deep learning. Eur. Urol. 76 (6), 714–718. https://doi.org/10.1016/j.eururo.2019.08.032 (2019).
Ahmed, F., Fatima, A., Mamoon, M. & Khan, S. Identification of the Diabetic Retinopathy Using ResNet-18, in 2nd Worldwide Convention on Cyber Resilience, ICCR Dubai, United Arab Emirates: IEEE, 2024, pp. 1–6. doi: (2024). https://doi.org/10.1109/ICCR61006.2024.10532925
Menaouer, B., Zoulikha, D., El-Houda, Okay. N., Mohammed, S. & Matta, N. Coronavirus pneumonia classification using X-Ray and CT scan pictures with deep convolutional neural community fashions. J. Inf. Technol. Res. 15 (1), 1–23. https://doi.org/10.4018/jitr.299391 (2022).
Menaouer, B., El-Houda, Okay. N., Zoulikha, D., Mohammed, S. & Matta, N. Detection and classification of mind tumors from MRI pictures using a deep convolutional neural Community Strategy. Int. J. Softw. Innov. 10 (1), 1–25. https://doi.org/10.4018/IJSI.293269 (2022).
Hesamian, M. H., Jia, W., He, X. & Kennedy, P. Deep learning methods for Medical Picture Segmentation: achievements and challenges. J. Digit. Imaging. 32 (4), 582–596. https://doi.org/10.1007/s10278-019-00227-x (2019).
Roth, H. R. et al. Deep learning and its utility to medical picture segmentation. Med. IMAGING Technol. 36 (2), 63–71. https://doi.org/10.11409/mit.36.63 (2018).
Mohammed, S. S., Menaouer, B., Zohra, A. F. F. & Nada, M. Sentiment evaluation of COVID-19 tweets using adaptive neuro-fuzzy inference system fashions. Int. J. Softw. Sci. Comput. Intell. 14 (1), 1–20. https://doi.org/10.4018/IJSSCI.300361 (2022).
Shen, D., Wu, G. & Suk, H. I. Deep learning in Medical Picture Evaluation. Annu. Rev. Biomed. Eng. 176 (1), 1–35. https://doi.org/10.1146/annurev-bioeng-071516-044442.Deep (2017).
Meijering, E. A hen ’ s-eye view of deep learning in bioimage evaluation. Comput. Struct. Biotechnol. J. 18, 2312–2325. https://doi.org/10.1016/j.csbj.2020.08.003 (2020).
Jia, X., Ren, L. & Cai, J. Scientific implementation of AI applied sciences would require interpretable AI fashions. Med. Phys. 47 (1), 1–4. https://doi.org/10.1002/mp.13891 (2020).
Karimkhani, C. et al. World skin disease morbidity and mortality an replace from the worldwide burden of disease examine 2013. JAMA Dermatology. 153 (5), 406–412. https://doi.org/10.1001/jamadermatol.2016.5538 (2017).
Seth, D., Cheldize, Okay., Brown, D. & Freeman, E. E. World burden of skin disease: inequities and improvements. Curr. Dermatol. Rep. 6 (3), 204–210. https://doi.org/10.1007/s13671-017-0192-7 (2017).
Chang, X. & Chen, M. Analysis progress of varicella and its immunoprophylaxis. Entrance. Med. Sci. Res. 4 (5), 36–39. https://doi.org/10.25236/FMSR.2022.040507 (2022).
Wutzler, P. et al. Varicella vaccination – the worldwide expertise. Professional Rev. Vaccines. 16 (8), 833–843 (2017).
Roy, Okay. et al. Skin disease detection based mostly on totally different segmentation methods, in Worldwide Convention on Opto-Electronics and Utilized Optics, Optronix 2019, Kolkata, India: IEEE, pp. 1–5. doi: (2019). https://doi.org/10.1109/OPTRONIX.2019.8862403
Daud, M. R. H. M., Yaacob, N. A., Ibrahim, M. I. & Muhammad, W. A. R. W. 5-Yr Pattern of measles and its Related elements inPahang, Malaysia: a Inhabitants-based examine. Int. J. Environ. Res. Public. Well being. 19, 1–10 (2022).
VON MAGNUS, S., ANDERSEN, E. Okay., PETERSEN, Okay. B. & AKSEI, B. A. A POX-LIKE DISEASE IN CYNOMOLGUS MONKEYS, FROM STATENS SEHUMINSTITUT, DIRECTOH J. OHSKOV, M.D., pp. 156–176, (1959).
Ladnyj, I. D., Ziegler, P. & Kima, E. A human an infection brought on by monkeypox virus in Basankusu Territory, Democratic Republic of the Congo. Bull. World Well being Organ. 46 (5), 593–597 (1972).
Reynolds, M. G., Doty, J. B., McCollum, A. M., Olson, V. A. & Nakazawa, Y. Monkeypox re-emergence in Africa: a name to broaden the idea and follow of 1 well being. Professional Rev. Anti Infect. Ther. 17 (2), 129–139. https://doi.org/10.1080/14787210.2019.1567330 (2019).
Koenig, Okay. L., Beÿ, C. Okay. & Marty, A. M. Monkeypox 2022 identify-Isolate-Inform: a 3I Instrument for frontline clinicians for a zoonosis with escalating human group transmission. One Heal. 15, 1–13. https://doi.org/10.1016/j.onehlt.2022.100410 (2022).
W. H. O. (WHO), Multi-country monkeypox outbreak in non-endemic international locations: Replace. Accessed: Sep. 04, 2022. [Online]. Accessible: https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON388
Ali, S. N. et al. Monkeypox Skin Lesion Detection Using Deep Learning Fashions: A Feasibility Research, Comput. Vis. Sample Recognit., pp. 2–5, [Online]. Accessible: (2022). http://arxiv.org/abs/2207.03342
Gülmez, B. MonkeypoxHybridNet: A hybrid deep convolutional neural community mannequin for monkeypox disease detection, Int. Res. Eng. Sci., vol. 3, pp. 49–64, [Online]. Accessible: (2022). https://desytamara.blogspot.com/2017/11/sistem-pelayanan-perpustakaan-dan-jenis.html%0Ahttps://lambeturah.id/pengertian-website-secara-umum-dan-menurut-para-ahli/%0Ahttps://www.researchgate.net/publication/269107473_What_is_governance/link/548173090cf2252
Irmak, M. C., Aydın, T. & Yağanoğlu, M. Monkeypox Skin Lesion Detection with MobileNetV2 and VGGNet Fashions, in TIPTEKNO 2022 – Medical Applied sciences Congress, Proceedings, Antalya, Turkey, pp. 2–5. doi: (2022). https://doi.org/10.1109/TIPTEKNO56568.2022.9960194
Singh, U. & Songare, L. S. Evaluation and Detection of Monkeypox using the GoogLeNet Mannequin, in In Proceedings of the Worldwide Convention on Automation, Computing and Renewable Techniques (ICACRS), Pudukkottai, India, 2022, pp. 1000–1008. doi: (2022). https://doi.org/10.1109/ICACRS55517.2022.10029125
Sharma, Okay., Kishlay, V., Kumar & Mittal, M. MonkeyPox, Measles and ChickenPox Detection by Picture-Processing using Residual Neural Community (ResNet), in sixth Worldwide Convention on Data Techniques and Pc Networks, ISCON 2023, Mathura, India: IEEE, 2023, pp. 1–6. doi: (2023). https://doi.org/10.1109/ISCON57294.2023.10112085
Sethy, P. Okay. et al. Detection of Monkeypox Primarily based on Improved Darknet19, in IEEE eighth Worldwide Convention for Convergence in Expertise, I2CT 2023, Pune, India: IEEE, 2023, pp. 1–3. doi: (2023). https://doi.org/10.1109/I2CT57861.2023.10126170
Uysal, F. Detection of Monkeypox Disease from Human skin pictures with a Hybrid Deep Learning Mannequin. Diagnostics 13 (10), 1–23. https://doi.org/10.3390/diagnostics13101772 (2023).
Ariansyah, M. H., Winarno, S. & Sani, R. R. Monkeypox and Measles Detection using CNN with VGG-16 transfer learning. J. Comput. Res. Innov. 8 (1), 32–44. https://doi.org/10.3390/s23041783 (2023).
Kundu, D., Siddiqi, U. R. & Rahman, M. M. Imaginative and prescient Transformer based mostly Deep Learning Mannequin for Monkeypox Detection, in twenty fifth Worldwide Convention on Pc and Data Expertise (ICCIT), Cox’s Bazar, Bangladesh: IEEE, pp. 1021–1026. doi: (2023). https://doi.org/10.1109/iccit57492.2022.10054797
Akram, A. et al. SkinMarkNet: an automatic method for prediction of monkeyPox using picture information augmentation with deep ensemble learning fashions. Multimed Instruments Appl. 1–17. https://doi.org/10.1007/s11042-024-19862-w (2024).
Monkeypox Skin Pictures Dataset (MSID). | Kaggle. Accessed: Aug. 28, 2022. [Online]. Accessible: https://www.kaggle.com/datasets/dipuiucse/monkeypoxskinimagedataset
Simonyan, Okay. & Zisserman, A. Very deep convolutional networks for large-scale picture recognition, in Revealed as a convention paper at ICLR, pp. 1–14. (2015).
Althubiti, S. A., Alenezi, F., Shitharth, S., Sangeetha, Okay. & Reddy, C. V. S. Circuit Manufacturing Defect Detection Using VGG16 Convolutional Neural Networks, Wirel. Commun. Mob. Comput., vol. pp. 1–10, 2022, doi: (2022). https://doi.org/10.1155/2022/1070405
Doshi-Velez, F. & Kim, B. In direction of a Rigorous Science of interpretable machine learning. arXiv Prepr, pp. 1–13, (2017).
Bach, S. et al. On pixel-wise explanations for non-linear classifier choices by layer-wise relevance propagation. PLoS One. 10 (7), 1–46. https://doi.org/10.1371/journal.pone.0130140 (2015).
Böhle, M., Eitel, F., Weygandt, M. & Ritter, Okay. Layer-wise relevance propagation for explaining deep neural community choices in MRI-based Alzheimer’s disease classification. Entrance. Getting older Neurosci. 11, 1–17. https://doi.org/10.3389/fnagi.2019.00194 (2019).
Huang, X., Jamonnak, S., Zhao, Y., Wu, T. H. & Xu, W. A visible designer of layer-wise relevance propagation fashions. Eurographics Conf. Vis. 40 (3), 227–238 (2021).
Seliya, N., Khoshgoftaar, T. M. & Van Hulse, J. A examine on the relationships of classifier efficiency metrics, in twenty first IEEE Worldwide Convention on Instruments with Artificial Intelligence, Newark, NJ, USA, pp. 59–66. doi: (2009). https://doi.org/10.1109/ICTAI.2009.25