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- W4328007027 abstract "A kind of deadly skin tumor called melanoma is arguably very lethal since it accounts for the majority of skin cancer fatalities. Melanomas are often brown or black in color because melanocyte cells, which produce melanin, are the source of the disease. The DNA of skin cells is damaged by UV light, which is the main cause of melanomas. The analysis of the dermoscopy examination’s findings and comparison with medical sciences are frequently used in the manual diagnosis of melanoma cancer. Human subjectivity has a strong impact on manual detection, which renders it unreliable in some circumstances. In order to classify the outcomes of the dermoscopy test and to determine the results more precisely with a comparatively shorter amount of time, computer-assisted technology is required. Problem statement, planning, execution, and testing are the first steps in the creation of this application. To identify picture data, the research study combines deep machine learning approach using Convolutional Neural Network technique along with LeNet-5 architectural model. With a variable number of training and testing epochs and 44 photos from the training results, the experiment with the best success rate (93% in training and 100% in testing) required 176 images and 100 epochs of training data. Programming language such as Python with utilization of Keras library, which serves as the Tensorflow back-end, were used to construct this application." @default.
- W4328007027 created "2023-03-22" @default.
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- W4328007027 date "2023-01-24" @default.
- W4328007027 modified "2023-10-02" @default.
- W4328007027 title "Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer" @default.
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- W4328007027 doi "https://doi.org/10.1109/gcwot57803.2023.10064671" @default.
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