A convolutional neural network architecture for the recognition of cutaneous manifestations of COVID‐19

Mathur, Jyoti; Chouhan, Vikas; Pangti, Rashi; Kumar, Sharad; Gupta, Somesh

During the COVID‐19 pandemic, dermatologists reported an array of different cutaneous manifestations of the disease. It is challenging to discriminate COVID‐19‐related cutaneous manifestations from other closely resembling skin lesions. The aim of this study was to generate and evaluate a novel CNN (Convolutional Neural Network) ensemble architecture for detection of COVID‐19‐associated skin lesions from clinical images. An ensemble model of three different CNN‐based algorithms was trained with clinical images of skin lesions from confirmed COVID‐19 positive patients, healthy controls as well as 18 other common skin conditions, which included close mimics of COVID‐19 skin lesions such as urticaria, varicella, pityriasis rosea, herpes zoster, bullous pemphigoid and psoriasis. The multi‐class model demonstrated an overall top‐1 accuracy of 86.7% for all 20 diseases. The sensitivity and specificity of COVID‐19‐rash detection were found to be 84.2 ± 5.1% and 99.5 ± 0.2%, respectively. The positive predictive value, NPV and area under curve values for COVID‐19‐rash were 88.0 ± 5.6%, 99.4 ± 0.2% and 0.97 ± 0.25, respectively. The binary classifier had a mean sensitivity, specificity and accuracy of 76.81 ± 6.25%, 99.77 ± 0.14% and 98.91 ± 0.17%, respectively for COVID‐19 rash. The model was robust in detection of all skin lesions on both white and skin of color, although only a few images of COVID‐19‐associated skin lesions from skin of color were available. To our best knowledge, this is the first machine learning‐based study for automated detection of COVID‐19 based on skin images and may provide a useful decision support tool for physicians to optimize contact‐free COVID‐19 triage, differential diagnosis of skin lesions and patient care.