Skins cancer identification system of HAMl0000 skin cancer dataset using convolutional neural network

Cancer disease was one of the significant health problems in the world that cause deaths. Skin cancer is a type of cancer caused by damaged DNA and the body cannot recover the damage. This makes the cells begin to grow and divide uncontrollably. Skin cancer in Indonesia was ranks third after breast and neck cancer. Cases of skin cancer reach 5.9-7.8% of all types of cancer every year. This disease could be identified using the dermatoscopic image. Through those images, doctor decided the skin condition based on the characteristics that exist. Decision making in determining the skin condition depends on the ability and knowledge of the doctor. Therefore, a skin cancer identification system is needed to help make decisions in identifying skin cancer. Convolutional neural network (CNN) was used in this identification system because it can be detecting image and pattern. CNN work through three stages, that is convolutional layer, pooling layer, and fully-connected layer. This identification system is based on the dermoscopy image of HAM10000 skin cancer dataset. Based on this research, the accuracy of training and testing of skins cancer identification system are 80% and 78%.