A web-based mpox skin lesion detection system using state-of-the-art deep learning models considering racial diversity

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The recent ‘Mpox’ outbreak, formerly known as ‘Monkeypox’, has been a significant public health concern and spread to over 110 countries globally. The challenge of clinically diagnosing mpox early on is due, in part, to its similarity to other types of rashes. Computer-aided screening tools have been proven valuable in cases where Polymerase Chain Reaction (PCR) based diagnosis is not immediately available. Deep learning methods excel at learning complex data representations, but their efficacy largely depends on adequate training data. To address this challenge, we present the “Mpox Skin Lesion Dataset Version 2.0 (MSLD v2.0)” as a follow-up to the previously released open-access dataset, one of the first to contain mpox lesion images. This dataset contains images of patients with mpox and five other non-mpox classes (chickenpox, measles, hand-foot-mouth disease, cowpox, and healthy). We benchmark the performance of several state-of-the-art deep learning models, including VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB3, InceptionV3, and Xception, to classify mpox and other infectious skin diseases. We leverage transfer learning implemented with pre-trained weights generated from the HAM10000 dataset, an extensive collection of pigmented skin lesion images. To reduce the impact of racial bias, we employ a color space data augmentation method to increase skin color variability during training and present a rigorous assessment of the technique’s effectiveness in skin lesion classification; we achieved the best overall accuracy of 83.59±2.11%. Finally, the developed models are incorporated within a prototype web application to analyze uploaded skin images by a user and determine whether a subject is a suspected mpox patient. We believe that the presented tool can be of significant impact in screening of mpox in case of a future outbreak.