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This research compares two Convolutional Neural Network (CNN) models for digital image-based melanoma classification, utilizing a dataset of 17,805 images. The best-performing model achieved an accuracy of 93.18% and supports a web-based platform for rapid and non-invasive melanoma detection. The study highlights the effectiveness of CNNs in improving early detection of melanoma while addressing limitations related to image quality and dataset diversity.

Author

Putera Farrel Elian

Language

EN