Identification of Risk of Occurring Skin Cancer (Melanoma) Using Convolutional Neural Network (CNN)

Abstract

Skin cancer is one of the most common
malignancy in human, has drawn attention from researchers
around the world. As skin cancer can turn into fatal if not
treated in its earliest stages, the necessity of devising automated
skin cancer diagnosis system that can automatically detect skin
cancer efficiently in its earliest stage in a faster process than
traditional one is of crucial importance. In this paper, a
computer aided skin cancer diagnosis system based
Convolutional Neural Network method has been shown. Our
proposed system consists of five stages namely image
acquisition, image preprocessing, image segmentation, feature
extraction and classification We remove hair any noise from
the images using dull then use median filter to smoothen the
images. Next, k-means algorithm was applied for image
segmentation on the preprocessed images. Finally, the
segmented images were fed into CNN model for feature
extraction and classification. The developed system can classify
benign and melanoma type skin cancers from Dermoscopic
images as accurate as 80.47%. While developing the skin
cancer detection system, we compare accuracy score of other
models such as Artificial Neural Network (ANN), K-Nearest
Neighbor (KNN) and Random Forest with our proposed
system. The proposed method has been tested on ‘ISIC
Challenge 2016’ test dataset and an accuracy rate of 80.47%
was obtained for accurately classifying benign and malignant
skin lesions by our proposed model.