A Systematic Study And Approach On Detection Of Classification Of Skin Cancer Using Back Propagated Artificial Neural Networks
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Abstract
Skin cancer is the leading type of cancer that causes millions of deaths of human beings. Early identification and appropriate medications for new harmful skin malignancy cases are fundamental to guarantee a low death rate as the survival rate. Most of the related works are focusing on machine learning-based algorithms, but they provide the maximum accuracy and specificity. In the preprocessing stage, sharpening filter and smoothening filters are used to remove the noise along with enhancement operations. Then segmentation is used for efficient detection of the region of skin cancer. Finally, to achieve the maximum accuracy for classification back-propagated based artificial neural network (BP-ANN) developed for the classification of skin cancer with the spatially gray level dependency matrix (SGLD) features. The suggested research work can be effectively used for the classification of Benign and Melanoma skin cancers.
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