Skin cancer diagnosis based on optimized convolutional neural network

Artif Intell Med. 2020 Jan:102:101756. doi: 10.1016/j.artmed.2019.101756. Epub 2019 Nov 8.

Abstract

Early detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are several reasons that have bad impacts on the detection precision. Recently, the utilization of image processing and machine vision in medical applications is increasing. In this paper, a new image processing based method has been proposed for the early detection of skin cancer. The method utilizes an optimal Convolutional neural network (CNN) for this purpose. In this paper, improved whale optimization algorithm is utilized for optimizing the CNN. For evaluation of the proposed method, it is compared with some different methods on two different datasets. Simulation results show that the proposed method has superiority toward the other compared methods.

Keywords: Convolutional neural networks; Deep learning; Lévy flight; Skin cancer diagnosis; Whale optimization algorithm.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Carcinoma / diagnosis
  • Computer Simulation
  • Databases, Factual
  • Diagnosis, Computer-Assisted
  • Early Diagnosis
  • Humans
  • Image Processing, Computer-Assisted
  • Melanoma / diagnosis
  • Neural Networks, Computer*
  • Predatory Behavior
  • Skin Neoplasms / diagnosis*
  • Whales