Comparative approach for discovery of cancerous skin using deep structured learning

In recent days, skin cancer cases are significantly increasing due to ozone layer damage. Impacts of the ozone layer damage ultraviolet rays directly penetrate the human skin leading to skin cancer. For the above reason it is important to develop the new model to detect the skin cancer in early stage from the digital data and image processing techniques. The research in detection of skin cancer is highly active from the year 2016. In this paper we attempt both the machine learning and deep learning algorithm to detect the skin cancer to improve the detection accuracy and early diagnosis of patients. In this proposed model we use machine learning algorithms like Naive Bayes, decision tree and KNN in that decision tree algorithm outperforms the rest of the algorithms used with accuracy of 83%. To further improve the accuracy we proposed the deep learning approach such as convolutional neural network to automate the skin cancer detection. In this paper we also compare the model accuracy of 93.54%.