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- W3163981173 abstract "Now a days, skin cancer is well known reason for human death. abnormal skin cells growth is known as skin cancer ,these skin cells generated on human body which exposed to the sunlight, it can generate anywhere on the human body. At early stage, most of the cancers are curable. Hence, it is required to detect skin cancer at early stage to save patient life. It is possible to recognize skin cancer at early stage with advanced technology. We describe a novel framework for dermoscopy image identification that employs a deep learning algorithm and an objects encoding scheme. The deep representations of a rescaled dermoscopy picture, in particular, are first extracted using an unusually deep residual neural network that has been pre-trained on a large natural picture dataset. After that, local deep descriptors are gathered via order less visual statistic characteristics, which are then used to generate a global picture representation using fisher vector encoding. Finally, using a convolution neural network, we used the fisher matrix encoded models to organize melanoma photos (CNN). With limited training data, our suggested system may produce additional discriminative information to deal with big differences among melanoma groups as well as minor differences between melanoma and non-melanoma classes." @default.
- W3163981173 created "2021-06-07" @default.
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- W3163981173 date "2021-05-27" @default.
- W3163981173 modified "2023-09-23" @default.
- W3163981173 title "A Systematic Analysis and Design of Skin Melanoma Detection System using Machine Learning" @default.
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