A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity | PLOS ONE
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GitHub - temcavanagh/Skin-Cancer-Detection: Implementing and comparing ResNet50 and MobileNetV2 transfer learning models using the MNIST:HAM10000 image dataset. Resulting classification accuracy of ~90%.
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PDF] Segmentation of Both Diseased and Healthy Skin From Clinical Photographs in a Primary Care Setting | Semantic Scholar
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Characteristics of publicly available skin cancer image datasets: a systematic review - The Lancet Digital Health
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The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions | Scientific Data
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PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones - ScienceDirect
GitHub - MRE-Lab-UMD/abd-skin-segmentation: Deep learning techniques for skin segmentation on novel abdominal dataset. Work conducted as part of the development process of an autonomous robotic ultrasound system.
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