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@article{155178,
        author = {Dr.C.MAHIBA and Indhumathi E and Kaleeswari P and Kanagavalli A},
        title = {classification of melanoma using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {255-259},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155178},
        abstract = {Dermatological diseases are one of the most pressing medical concerns in the twenty-first century. Owing to their high complexity and cost of diagnosis as well as the difficulty and subjectivity of human interpretation in the case of lethal disorders such as melanoma early detection is critical in evaluating the likelihood of cure. We believe those using automated methods will aid in early diagnosis particularly when dealing with a large number of photos with a variety of diagnoses as a result we describe In this article a fully automated approach for recognising dermatological disease through lesion photographs a machine intervention in contrast to traditional medical personnel-based identification. We proposed an approach to detect the melanoma skin cancer and feature extraction through various image processing techniques. Our model is divided into three stages data gathering and augmentation model creation and prediction. We applied a variety of AI techniques including convolution neural network and support vector machine and amalgamated it with image processing tools to form a better structure leading to higher accuracy of 85% .},
        keywords = {Data gathering, Augmentation model, prediction, AI Techniques},
        month = {},
        }