An Intelligent Inflammatory Skin Lesions Classification Scheme for Mobile Devices

Nazia Hameed, Antesar Shabut, Fozia Hameed, Silvia Cirestea, Alamgir Hossain

Research output: Contribution to conferencePaper

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
Pages83-88
Number of pages6
DOIs
Publication statusPublished - 22 Aug 2019
Event2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE) - London, United Kingdom
Duration: 22 Aug 201923 Aug 2019

Conference

Conference2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE)
Abbreviated titleiCCECE
CountryUnited Kingdom
CityLondon
Period22/08/1923/08/19

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  • Cite this

    Hameed, N., Shabut, A., Hameed, F., Cirestea, S., & Hossain, A. (2019). An Intelligent Inflammatory Skin Lesions Classification Scheme for Mobile Devices. 83-88. Paper presented at 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE), London, United Kingdom. https://doi.org/10.1109/iCCECE46942.2019.8941851