DIAGNOSIS OF MELANOMA SKIN CANCER USING NEURAL NETWORK
Abstract
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.
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