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- W4387231356 abstract "Melanoma skin cancer is one of the most dangerous and life-threatening cancer. Exposure to ultraviolet rays may damage the skin cell's DNA, which causes melanoma skin cancer. However, it is difficult to detect and classify melanoma and nevus mole at the immature stages. In this work, an automatic deep learning system is developed based on the intensity value estimation with a convolutional neural network model (CNN) to detect and classify melanoma and nevus mole more accurately. Since intensity levels are the most distinctive features for object or region of interest identification, the high-intensity pixel values are selected from the extracted lesion images. Incorporating those high-intensity features into the CNN improves the overall performance than the state-of-the-art methods for detecting melanoma skin cancer. To evaluate the system, we used 5-fold cross-validation. Experimental results show that a superior percentage of accuracy (92.58%), Sensitivity (93.76%), Specificity (91.56%), and Precision (90.68%) are achieved." @default.
- W4387231356 created "2023-10-02" @default.
- W4387231356 creator A5054073322 @default.
- W4387231356 creator A5087775249 @default.
- W4387231356 date "2023-10-01" @default.
- W4387231356 modified "2023-10-16" @default.
- W4387231356 title "Melanoma Skin Cancer and Nevus Mole Classification using Intensity Value Estimation with Convolutional Neural Network" @default.
- W4387231356 doi "https://doi.org/10.7494/csci.2023.24.3.4844" @default.
- W4387231356 hasPublicationYear "2023" @default.
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