Evaluation of Big Data based CNN Models in Classification of Skin
Lesions with Melanoma
- URL: http://arxiv.org/abs/2007.05446v1
- Date: Fri, 10 Jul 2020 15:39:32 GMT
- Title: Evaluation of Big Data based CNN Models in Classification of Skin
Lesions with Melanoma
- Authors: Prasitthichai Naronglerdrit, Iosif Mporas
- Abstract summary: The architecture is based on convolu-tional neural networks and it is evaluated using new CNN models as well as re-trained modification of pre-existing CNN models were used.
The best performance was achieved by re-training a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%.
- Score: 7.919213739992465
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This chapter presents a methodology for diagnosis of pigmented skin lesions
using convolutional neural networks. The architecture is based on
convolu-tional neural networks and it is evaluated using new CNN models as well
as re-trained modification of pre-existing CNN models were used. The
experi-mental results showed that CNN models pre-trained on big datasets for
gen-eral purpose image classification when re-trained in order to identify skin
le-sion 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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