… analysis of six different transferlearning nets for multi-class skincancerclassification by taking the … The transferlearning nets that were used in the analysis were VGG19, InceptionV3, …
… We found that the majority of biomedical imaging transferlearning methods used VGG approaches to achieve the highest levels of prediction accuracy after thoroughly analyzing the …
… deep learning artificial intelligence. The work in this paper examines the applicability of raw deep transferlearning in classifying images of skin … using 13 deep transferlearning models. …
… transferlearning driven deep IoHT framework for detection and classification of skin lesions or cancer… In this proposed framework, automatic features are being extracted from skin lesion …
DNT Le, HX Le, LT Ngo, HT Ngo - arXiv preprint arXiv:2009.05977, 2020 - arxiv.org
… Model Architectures We applied transferlearning for skin lesion classification by slightly modifying architecture and fine-tuning weights of the ResNet50 [34] models pre-trained on the …
… , binary class classification is performed by utilizing the transferlearning model on epoch 6, 7… For skincancerclassification, we calculated the output achieved from the epoch 6, 7, and 8 …
KM Hosny, MA Kassem, MM Foaud - PloS one, 2019 - journals.plos.org
… skin lesions classification system with higher classification rate using the theory of transfer learning … outperformed the performance of the existing classification methods of skincancer. …
KM Hosny, MA Kassem, MM Fouad - Journal of digital imaging, 2020 - Springer
… classify different types of skincancer [6]. The DIP methods will increase the agility and reliability of lesion diagnosis than those dermatologists that have high skills. In the DIP methods, …
MA Kassem, KM Hosny, MM Fouad - IEEE Access, 2020 - ieeexplore.ieee.org
… apply transferlearning by removing only the last two layers, SoftMax and classification output, … , MA Kassem, and MM Foaud, ‘‘Skincancerclassification using deep learning and transfer …