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A comparative study for skin lesion segmentation and melanoma detection where deep learning methods can perform very well without complex pre-processing techniques except for normalization and augmentation.

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nahin333/A-Comparative-Study-of-Neural-Network-Architectures-for-Lesion-Segmentation-and-Melanoma-Detection

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Deep learning applied with augmentation can save us from complex pre-processing steps and it studies the performance of different neural network architectures for lesion segmentation as an integral part of image processing-based techniques and finally evaluates network architectures for melanoma detection on dermoscopic images using transfer learning and presents a comparative view of those studies.

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A comparative study for skin lesion segmentation and melanoma detection where deep learning methods can perform very well without complex pre-processing techniques except for normalization and augmentation.

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