RSWINV2-MD: AN ENHANCED RESIDUAL SWINV2 TRANSFORMER FOR MONKEYPOX DETECTION FROM SKIN IMAGES

Abstract

Monkeypox (MPox) has recently become a rising global health threat due to its efficiency in being transmitted via direct contact with the skin. The accuracy of lesion observation in its early stages has been critical in containing the outbreak of the virus; however, due to its high infectivity and development of new strains, monitoring and screening have become more difficult. In this paper, a deep learning approach for Mpox diagnosis named "Customized Residual SwinTransformerV2 (RSwinV2) has been proposed, trying to enhance the capability of lesion classification by employing the RSwinV2 tool-assisted vision approach. In the RSwinV2 method, a hierarchical structure of the transformer has been customized based on the input dimensionality, embedding structure, and output targeted by the method. In this RSwinV2 approach, the input image has been split into non-overlapping patches and processed using shifted windows and attention in these patches. This process has helped the method link all the windows efficiently by avoiding the locality issues of non-overlapping regions in attention, while being computationally efficient. RSwinV2 has further developed based on SwinTransformer and has included patch and position embeddings to take advantage of the transformer’s global-linking capability by employing multi-head attention in these embeddings. Furthermore, RSwinV2 has developed and incorporated the Inverse Residual Block (IRB) into this method, which utilizes convolutional skip connections with these inclusive designs to address the vanishing gradient issues during processing. RSwinV2’s inclusion of IRB has therefore facilitated this method to link global patterns as well as local patterns; hence, its integrity has helped improve lesion classification capability by minimizing variability of Mpox and increasing differences of Mpox, chickenpox, measles, and cowpox. In testing RSwinV2, its accuracy of 96.51% and F1 of 96.13% have been achieved on the Kaggle public dataset, which has outperformed standard CNN models and SwinTransformers; RSwinV2’s vector has thus proved its valiance as a computer-assisted tool for Mpox lesion observation interpretation.