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- W4213175656 abstract "Melanoma broadcasts by metastasis, and consequently, it is determined to be very disastrous. The majority of deaths from carcinoma are an outcome of melanoma. Statistical shreds of evidence are proving the same. Further investigations have pointed that before time spotting and intercession of melanoma entail more eminent plausibility of cure. Clinical diagnoses of skin lesions are challenging doctor’s subjectivity, therefore, resulting in misdiagnosis and inaccuracies. Posthaste diagnosis and enhanced accuracy can be gained by an automatic model saving high-priced time. Here, an intelligent retrieval method adopting a convolutional neural network (CNN) for listing seven varieties of skin infections is put forth. Transfer learning, besides CNN, is adopted that grows the classification accuracy on the International Skin Imaging Collaboration (ISIC) data-set. The focus is on the foremost efficient DNNs for image processing: VGG16 and Residual Networks (ResNet). The comparison of the CNN structures concerning the accuracy was analyzed. CNN alone gave an accuracy of 92%, VGG16 gave 97%, and the ResNet model had an accuracy of 98.86%. Image error checking tools are applied for heightening lesion field. The statistical pieces of information in an image are drawn from Gray Level Co-occurrence Matrix." @default.
- W4213175656 created "2022-02-24" @default.
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- W4213175656 creator A5072018008 @default.
- W4213175656 date "2021-12-10" @default.
- W4213175656 modified "2023-09-24" @default.
- W4213175656 title "Skin Lesion Analysis for Classifying Melano" @default.
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- W4213175656 doi "https://doi.org/10.1109/iceeccot52851.2021.9707924" @default.
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