Deep Convolutional Neural Network for Melanoma Detection using Dermoscopy Images

Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul:2020:1524-1527. doi: 10.1109/EMBC44109.2020.9175391.

Abstract

Developing a fast and accurate classifier is an important part of a computer-aided diagnosis system for skin cancer. Melanoma is the most dangerous form of skin cancer which has a high mortality rate. Early detection and prognosis of melanoma can improve survival rates. In this paper, we propose a deep convolutional neural network for automated melanoma detection that is scalable to accommodate a variety of hardware and software constraints. Dermoscopic skin images collected from open sources were used for training the network. The trained network was then tested on a dataset of 2150 malignant or benign images. Overall, the classifier achieved high average values for accuracy, sensitivity, and specificity of 82.95%, 82.99%, and 83.89% respectively. It outperfomed other exisitng networks using the same dataset.

MeSH terms

  • Dermoscopy
  • Diagnosis, Computer-Assisted*
  • Humans
  • Melanoma* / diagnostic imaging
  • Neural Networks, Computer
  • Skin Neoplasms* / diagnostic imaging