DIAGNOSIS OF MELANOMA SKIN CANCER USING NEURAL NETWORK - Sivaranjani, N.; Kalaimani, Ms.M. (IJRDO-Journal of Applied Science, 2015)
Malignant melanoma is the appearance of sores that cause bleeding and is the deadliest form of skin cancer. Incidence rates of melanoma have been increasing, but survival rates are high if detected early. With the advancement of technology, early detection of skin cancer is possible. The proposed framework consists of image segmentation, followed by comprehensive feature set extraction and neural network classification with higher segmentation accuracy. The system uses enhanced image processing to segment the images without manual intervention. From the segmented image, it extracts a comprehensive set of pixel features and RGB colour components of the digital image. As the experience and training-based learning is an important characteristic of neural networks, the features were fed automatically to a BackPropagation Neural Network (BPNN) classifier. It classifies the given data set into cancerous or non-cancerous to identify the skin cancer easily.