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- W4306754161 abstract "Given the success of Deep Convolutional Neural Network in Computer Vision tasks such as image classification, object detection, etc. ,DCNN has been applied to many other fields and lays the path for new research domains. Recently, by transfer learning, Esteva et al proposed in “Dermatologist – level classification of Skin Cancer with Deep Neural Networks” that “CNN achieves performance on par with all tested experts, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists”. The results of experiments verify the intuition that features learned by pretrained models and the architectures of the DCNNs help learning features for a completely different domain dataset, here is the skin lesions dermatoscopic images dataset. Given the computational time and the test accuracy of fine-tuning the top layers and fine-tuning the whole model, for this particular dataset, I find that it’s better to fine-tune the whole pretrained model with fewer epochs and less computational time and achieve better accuracy.[1]." @default.
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- W4306754161 date "2022-10-18" @default.
- W4306754161 modified "2023-09-30" @default.
- W4306754161 title "Skin Lesions Classification and Prediction with Deep CNN" @default.
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- W4306754161 doi "https://doi.org/10.48175/ijarsct-7181" @default.
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