Diagnosis of various skin cancer lesions based on fine-tuned ResNet50 deep network

With the massive success of deep networks, there have been signi-
cant efforts to analyze cancer diseases, especially skin cancer. For this purpose,
this work investigates the capability of deep networks in diagnosing a variety
of dermoscopic lesion images. This paper aims to develop and ne-tune a
deep learning architecture to diagnose different skin cancer grades based on
dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced
classication results by the customized pre-trained network. Regularization,
batch normalization, and hyperparameter optimization are performed for
ne-tuning the proposed deep network. The proposed ne-tuned ResNet50
model successfully classied 7-respective classes of dermoscopic lesions using
the publicly available HAM10000 dataset. The developed deep model was
compared against two powerful models, i.e., InceptionV3 and VGG16, using
the Dice similarity coefcient (DSC) and the area under the curve (AUC). The
evaluation results show that the proposed model achieved higher results than
some recent and robust models.