RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis

Monkeypox (MPox) has emerged as a significant global concern, with cases steadily increasing daily. Conventional detection methods, including polymerase chain reaction (PCR) and manual examination, exhibit challenges of low sensitivity, high cost, and substantial workload. Therefore, deep learning offers an automated solution; however, the datasets include data scarcity, texture, contrast, inter-intra class variability, and similarities with other skin infectious diseases. In this regard, a novel hybrid approach is proposed that integrates the learning capacity of Residual Learning and Spatial Exploitation Convolutional Neural Network (CNN) with a customized Swin Transformer (RS-FME-SwinT) to capture multi-scale global and local correlated features for MPox diagnosis. The proposed RS-FME-SwinT technique employs a transfer learning-based feature map enhancement (FME) technique, integrating the customized SwinT for global information capture, residual blocks for texture extraction, and spatial blocks for local contrast variations. Moreover, incorporating new inverse residual blocks within the proposed SwinT effectively captures local patterns and mitigates vanishing gradients. The proposed RS-FME-SwinT has strong learning potential of diverse features that systematically reduce intra-class MPox variation and enable precise discrimination from other skin diseases. Finally, the proposed RS-FME-SwinT is a holdout cross-validated on a diverse MPox dataset and achieved outperformance on state-of-the-art CNNs and ViTs. The proposed RS-FME-SwinT demonstrates commendable results of an accuracy of 97.80%, sensitivity of 96.82%, precision of 98.06%, and an F-score of 97.44% in MPox detection. The RS-FME-SwinT could be a valuable tool for healthcare practitioners, enabling prompt and accurate MPox diagnosis and contributing significantly to mitigation efforts.

A Recent Survey of the Advancements in Deep Learning Techniques for Monkeypox Disease Detection

Monkeypox is a zoonotic infectious disease induced by the Monkeypox 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 monkeypox as a global public health pandemic, in July-2022. Timely diagnosis is imperative for assessing disease severity, conducting clinical evaluations, and determining suitable treatment plans. 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 during public health emergencies, particularly in the case of epidemics and pandemics. Therefore, this survey paper provides an extensive and efficient analysis of deep learning (DL) methods for the automatic detection of MP 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 MP. 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 study and is anticipated to serve as a path for researchers.