Pal Madhumita, Mahal Ahmed, Mohapatra Ranjan K, Obaidullah Ahmad J, Sahoo Rudra Narayan, Pattnaik Gurudutta, Pattanaik Sovan, Mishra Snehasish, Aljeldah Mohammed, Alissa Mohammed, Najim Mustafa A, Alshengeti Amer, AlShehail Bashayer M, Garout Mohammed, Halwani Muhammad A, Alshehri Ahmad A, Rabaan Ali A
Department of Electrical Engineering, Government College of Engineering, Keonjhar, Odisha 758 002, India.
Department of Medical Biochemical Analysis, College of Health Technology, Cihan University-Erbil, Erbil, Kurdistan Region, Iraq.
ACS Omega. 2023 Aug 23;8(35):31747-31757. doi: 10.1021/acsomega.3c02784. eCollection 2023 Sep 5.
The world faces multiple public health emergencies simultaneously, such as COVID-19 and Monkeypox (mpox). mpox, from being a neglected disease, has emerged as a global threat that has spread to more than 100 nonendemic countries, even as COVID-19 has been spreading for more than 3 years now. The general mpox symptoms are similar to chickenpox and measles, thus leading to a possible misdiagnosis. This study aimed at facilitating a rapid and high-brevity mpox diagnosis. Reportedly, mpox circulates among particular groups, such as sexually promiscuous gay and bisexuals. Hence, selectively vaccinating, isolating, and treating them seems difficult due to the associated social stigma. Deep learning (DL) has great promise in image-based diagnosis and could help in error-free bulk diagnosis. The novelty proposed, the system adopted, and the methods and approaches are discussed in the article. The present work proposes the use of DL models for automated early mpox diagnosis. The performances of the proposed algorithms were evaluated using the data set available in public domain. The data set adopted for the study was meant for both training and testing, the details of which are elaborated. The performances of CNN, VGG19, ResNet 50, Inception v3, and Autoencoder algorithms were compared. It was concluded that CNN, VGG19, and Inception v3 could help in early detection of mpox skin lesions, and Inception v3 returned the best (96.56%) classification accuracy.
世界同时面临多种突发公共卫生事件，如新冠疫情和猴痘。猴痘曾是一种被忽视的疾病，如今已成为全球威胁，传播至100多个非流行国家，而新冠疫情已持续传播三年多。猴痘的一般症状与水痘和麻疹相似，因此可能导致误诊。本研究旨在实现快速、简洁的猴痘诊断。据报道，猴痘在特定群体中传播，比如性乱交的男同性恋者和双性恋者。因此，由于相关的社会污名，对他们进行选择性疫苗接种、隔离和治疗似乎很困难。深度学习在基于图像的诊断方面前景广阔，有助于进行无差错的批量诊断。本文讨论了所提出的新颖之处、采用的系统以及方法和途径。目前的工作提出使用深度学习模型进行猴痘的自动早期诊断。使用公共领域可用的数据集对所提出算法的性能进行了评估。本研究采用的数据集用于训练和测试，其详细信息将予以阐述。比较了卷积神经网络（CNN）、VGG19、残差网络50（ResNet 50）、Inception v3和自动编码器算法的性能。得出的结论是，CNN、VGG19和Inception v3有助于早期检测猴痘皮肤病变，Inception v3的分类准确率最高（96.56%）。
