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Detection and Diagnosis of Skin Cancer Based On K-Means Cluster and Convolutional Neural Network

Early detection of skin cancer can contribute significantly in preventing the exacerbation of the condition and its transformation into a benign tumor, reducing the number of deaths in skin cancer. This paper introduces the application of K-means cluster for detecting the skin tumor and the Convolutional Neural Network (CNN) for automatic diagnosis of skin tumor. The specific steps are as follows: The first step reads the skin cancer image, the second step “preprocessing, which is achieved by applying the Gaussian filter, to improve the skin image and reduce noise, the third step: utilizing K-means cluster applied for segmentation image into two groups. The first group is represented by the background (largest area) and second group represents the skin tumor (least area). The last step (The fourth one): applying CNN to classify the skin tumor as benign or malignant. The results show that the proposed technique functions more efficiently by achieving 99.7% accuracy, obtained from the total of 22,080 medical images based on DermQuest Digital Dataset.