An Efficient Framework using Deep Learning for Skin Cancer Classification

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A good image analysis model can be very helpful in accurate diagnosis/classification of diseases for which images are available. Due to a plethora of public image databases, training and testing of algorithms on the dataset have helped in the development of an efficient framework for image classification. Skin cancer is one such disease for which recently image databases have been developed. Of the various methods of classification of skin cancer based on image analysis, convolutional neural network (CNN) has proven to be better performing than conventional machine learning approach. Realizing the importance of developing an efficient framework for skin cancer classification, this paper proposes a framework which utilizes VGG-16 CNN model to classify cancer images into categories namely, malignant or benign. We have trained the model using the skin cancer images freely accessible on the ISIC archive and attained an accuracy of 97.81%.




Garg*, S. … Pant, K. (2019). An Efficient Framework using Deep Learning for Skin Cancer Classification. International Journal of Innovative Technology and Exploring Engineering, 2(9), 1947–1951.

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