Abstract
Melanoma is the most dangerous form of skin cancer that often looks like moles. Dermatologists often recommend a regular examination of the skin to identify eliminate the melanoma in its early stages. To facilitate this process, we present an embedded skin cancer classification on a Raspberry Pi. This system provides a real-time classification of lesion taken by an embedded pi camera. The classification model used is deployed using a dataset issued from the ISIC2017 challenge. The model was first created on a computer and serialized to the raspberry pi. Features used are those used by dermatologist based on skin and lesion color and texture information. SVM is used as the classification algorithm. Experimentation results show the effectiveness of our proposed classification implementation.
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Ibrahim, K., Filali, Y., Sabri, M.A., Aarab, A. (2021). Embedded Skin Cancer Detection and Classification on Raspberry Pi. In: Masrour, T., El Hassani, I., Cherrafi, A. (eds) Artificial Intelligence and Industrial Applications. A2IA 2020. Lecture Notes in Networks and Systems, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-030-53970-2_28
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DOI: https://doi.org/10.1007/978-3-030-53970-2_28
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