ABSTRACT
Skin disease also known as melanoma it is one of the deadliest form of disease if not recognized in time. Since the pigmented areas/moles of the skin can be nicely observed by simple, non-invasive visual inspection the clinical protocols of its recognition also consider several visual features. Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, melanoma classification for skin disease diagnosis. Activation functions play an important role in the performance of convolutional neural networks for image recognition problems as well as medical image classification. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration on cheeks is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this project, different imaging techniques like preprocessing method and classification are used to analyze and extract the information of skins discoloration disease from skin images. This project is developed in matlab using convolutional neural network cnn.
PROJECT OUTPUT
PROJECT DEMO VIDEO
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