An intelligent inflammatory skin lesions classification scheme for mobile devices

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    Illness directly affecting the skin is the fourth most frequent cause of all human disease, and is seeking the attention of researchers. In this research work, one such effort is made by proposing a mobile-enabled expert system named “i-Rash” for the classification of inflammatory skin lesions. i-Rash can classify the skin image into one of the four non-overlapping classes, i.e. healthy, acne, eczema, and psoriasis. The classification model for i-Rash is trained using deep learning model SqueezeNet. The pre-trained SqueezeNet is re-trained on the skin image dataset using transfer learning approach. The i-Rash classification model is trained and tested on 1856 images. The trained model is only of 3MB size and is capable of classifying an unseen image in a fraction of seconds with an accuracy, sensitivity, and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash is based on a client-server architecture and can serve in initial classification of skin lesions, hence, can play a very important role in minimising the global burden caused by skin diseases.
    Original languageEnglish
    Publication statusUnpublished - 22 Aug 2019
    EventIEEE International Conference on Computing, Electronics & Communications Engineering 2019 - London Metropolitan University, UK, London, United Kingdom
    Duration: 22 Aug 201923 Aug 2019
    http://www.iccece19.theiaer.org

    Conference

    ConferenceIEEE International Conference on Computing, Electronics & Communications Engineering 2019
    Country/TerritoryUnited Kingdom
    CityLondon
    Period22/08/1923/08/19
    Internet address

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