02943nas a2200265   4500000000100000000000100001008004100002260001200043653002300055653001400078653002100092653002300113653002200136100001400158700002600172700001600198700001800214700002300232245011000255856005800365300000900423490001300432520221800445022001402663       9998                            d  c11/202410aMobile Application10aMonkeypox10aSwin Transformer10aSkin Lesion Images10aTransfer Learning1 aS. Sadesh1 aRajasekaran Thangaraj1 aP. Pandiyan1 aR. Devi Priya1 aPalanichamy Naveen00aIdentification of Monkeypox Disease Based on MpoxNet and Swin Transformer Models Using Mobile Application  uhttps://www.ijimai.org/journal/bibcite/reference/3508  a1-130 vIn press3 aHumankind is still reeling from the devastating impact of the Covid-19 pandemic, yet another looming threatis the potential global spread of the monkeypox virus. While monkeypox may not pose the same level of lethality or contagion as COVID-19, its significant spread across countries is cause for concern. Already, outbreaks have been reported in 75 nations worldwide. Despite sharing clinical characteristics with smallpox, including lesions and rashes, monkeypox symptoms are frequently mistaken for those of other poxviruses such as chickenpox and cowpox. Consequently, accurate early diagnosis of monkeypox by healthcare professionals remains challenging. Automated monkeypox identification using Deep Learning (DL) techniques presents a promising avenue for addressing this challenge. In this study, a modified deep convolutional neural network (DCNN) model named MpoxNet is proposed for the identification of monkeypox disease. The performance of MpoxNet is evaluated against established DCNN models, including ResNet50, VGG16, VGG19, DenseNet121, DenseNet169, Xception, InceptionResNetV2, and MobileNetV2. This study addresses the pressing challenge of monkeypox 
identification by proposing MpoxNet. With the aim of enhancing early detection and containment efforts, MpoxNet's performance is evaluated against established DCNN models across two distinct datasets: MSLD and MSID Dataset. Results reveal MpoxNet's superior test accuracy of 94.82% on the MSLD Dataset, surpassing other models. However, evaluation on the MSID Dataset highlights variations in performance, emphasizing the influence of dataset characteristics. Additionally, the introduction of the Swin Transformer model demonstrates exceptional performance on the MSLD and the MSID Dataset and, achieving an accuracy of 98%. These findings underscore the importance of considering diverse datasets and leveraging advanced techniques for robust monkeypox detection systems. Integration of MpoxNet with a mobile application offers a promising solution for rapid and precise monkeypox disease detection, providing valuable insights for future research and real-world deployment strategies to effectively combat the global spread of monkeypox.  a1989-1660