Abstract
That chapter presents a method regarding diagnosis of pigmented skin lesions using convolutional neural networks. The architecture is modeled over convolutional neural networks and it is evaluated using new CNN models as well as re-trained modification of pre-existing CNN models were used. The experimental results showed that CNN models pre-trained on big datasets for general purpose image classification when re-trained in order to identify skin lesion types offer more accurate results when compared to convolutional neural network models trained explicitly from the dermatoscopic images. The best performance was achieved by re-training a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%. Analysis on skin lesion pathology type was also performed with classification accuracy for melanoma and basal cell carcinoma being equal to 79.13 and 82.88%, respectively.
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Naronglerdrit, P., Mporas, I. (2021). Evaluation of Big Data Based CNN Models in Classification of Skin Lesions with Melanoma. In: Kose, U., Alzubi, J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_5
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