Skin Cancer Classification using Deep Learning

Keerthana R; Adithya K; Aaisha  B; Abuhasan A; Ajith Kumar  S

doi:10.34256/ijcci2415

Keerthana R Department of Biomedical Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
Adithya K Department of Biomedical Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
Aaisha B Department of Biomedical Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
Abuhasan A Department of Biomedical Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
Ajith Kumar S Department of Biomedical Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

Keywords: Deep Learning, Convolutional Neural Networks (CNN), Basal Cell Carcinoma, Melanoma, Nevus, Flask

Abstract

According to world health organization skin cancer is the one of the most common cancer types in the world. The abnormal growth of skin cells most often develops on the skin when exposed to the sun and occurs when there is a mutation in the DNA of skin cells, it begins at the top of the skin. More than five million people are affected by skin cancer each year. The proposed method aim at analyzing and detecting the significant class of skin cancer variant such as Melanoma, Basal cell Carcinoma, Nevus. Melanoma is the most dangerous form of skin cancer when compared to the other types. In this paper we have developed a webapp that could differentiate skin cancer. The data set has been taken from ISIC and the model is trained using Gcollab. The proposed work has used convolution neural network (CNN) as algorithm for deep learning as it has higher accuracy and flask is used to develop the web app and the class of cancer is classified based on historical data of dermoscopic images.

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