Skin Cancer Classification and Detection Using VGG-19 and DesNet

Abstract

Skin cancer is an alarming situation. Over 150 thousand cases of skin cancer have been detected around the world. It is necessary that skin cancer is detected and diagnosed in its initial stage. The system proposed in this paper performs lesion segmentation and classification of cancer by taking dermatoscopic images as input. Skin lesion segmentation system uses the BCDU-Net model. The dice co-efficient and IOU of segmentation system are 90.66% and 83.09% respectively. Performances of VGG-19 and the DenseNet model are compared for skin cancer classification. VGG-19 provides an accuracy of 97.29% which is considerably better than some of the previous models.

Topics & Concepts