Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma
This work addresses skin cancer diagnosis for medical applications, but it is incremental as it applies existing transfer learning methods to a specific domain.
The study tackled skin lesion classification by comparing CNN models, finding that pre-trained models on big datasets, when retrained, outperformed models trained from scratch on dermatoscopic images, achieving up to 93.89% accuracy for lesion types and 79.13% for melanoma.
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.