Monkeypox (MPox) is a zoonotic infectious disease induced by the MPox Virus, part of the poxviridae orthopoxvirus group initially discovered in Africa and gained global attention in mid-2022 with cases reported outside endemic areas. Symptoms include headaches, chills, fever, smallpox, measles, and chickenpox-like skin manifestations and the WHO officially announced MPox as a global public health pandemic, in July 2022.Traditionally, PCR testing of skin lesions is considered a benchmark for the primary diagnosis by WHO, with symptom management as the primary treatment and antiviral drugs like tecovirimat for severe cases. However, manual analysis within hospitals poses a substantial challenge including the substantial burden on healthcare professionals, limited facilities, availability and fatigue among doctors, and human error during public health emergencies. Therefore, this survey paper provides an extensive and efficient analysis of deep learning (DL) methods for the automatic detection of MPox in skin lesion images. These DL techniques are broadly grouped into categories, including deep CNN, Deep CNNs ensemble, deep hybrid learning, the newly developed, and Vision transformer for diagnosing MPox. Moreover, this study offers a systematic exploration of the evolutionary progression of DL techniques and identifies, and addresses limitations in previous methods while highlighting the valuable contributions and innovation. Additionally, the paper addresses benchmark datasets and their collection from various authentic sources, pre-processing techniques, and evaluation metrics. The survey also briefly delves into emerging concepts, identifies research gaps, limitations, and applications, and outlines challenges in the diagnosis process. This survey furnishes valuable insights into the prospective areas of DL innovative ideas and is anticipated to serve as a path for researchers.

翻译：猴痘（MPox）是一种由猴痘病毒（MPox Virus）引起的人畜共患传染病，属于痘病毒科正痘病毒属，最初发现于非洲，并于2022年中期因非流行地区报告病例而获得全球关注。其症状包括头痛、寒战、发热、天花样、麻疹样及水痘样皮肤表现，世界卫生组织（WHO）于2022年7月正式宣布猴痘为全球公共卫生紧急事件。传统上，WHO将皮肤病变的PCR检测作为主要诊断基准，对症治疗为主要疗法，重症病例则使用替考韦马特（tecovirimat）等抗病毒药物。然而，医院内的人工分析面临重大挑战，包括医护人员负担过重、设施有限、资源可用性不足、医生疲劳以及公共卫生紧急情况下的人为错误。为此，本文对皮肤病变图像中猴痘自动检测的深度学习（Deep Learning, DL）方法进行了广泛而高效的分析。这些DL技术大致分为以下类别：深度卷积神经网络（deep CNN）、深度CNN集成方法、深度混合学习、新近发展的方法以及用于诊断猴痘的视觉Transformer（Vision Transformer）。此外，本研究系统探讨了DL技术的演进历程，识别并解决了先前方法的局限性，同时强调了有价值的贡献与创新。本文还涵盖了基准数据集及其来自多种可靠来源的收集方法、预处理技术和评估指标。该综述简要探讨了新兴概念，识别了研究空白、局限性和应用场景，并概述了诊断过程中的挑战。本研究为DL创新思路的未来方向提供了宝贵见解，并有望为研究人员提供指引。