Publications by authors named "Vikas Chouhan"

3 Publications

  • Page 1 of 1

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

Dermatol Ther 2021 Mar 28;34(2):e14902. Epub 2021 Feb 28.

Department of Dermatology and Venereology, All India Institute of Medical Sciences, New Delhi, India.

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.
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http://dx.doi.org/10.1111/dth.14902DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995139PMC
March 2021

Performance of a deep learning-based application for the diagnosis of basal cell carcinoma in Indian patients as compared to dermatologists and nondermatologists.

Int J Dermatol 2021 Feb 11;60(2):e51-e52. Epub 2020 Oct 11.

Department of Dermatology & Venereology, All India Institute of Medical Sciences, New Delhi, India.

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http://dx.doi.org/10.1111/ijd.15242DOI Listing
February 2021