ResNet101 and DAE for Enhance Quality and Classification Accuracy in
  Skin Cancer Imaging
Sibasish Dhibar
Skin cancer is a crucial health issue that requires timely detection for
higher survival rates. Traditional computer vision techniques face challenges
in addressing the advanced variability of skin lesion features, a gap partially
bridged by convolutional neural networks (CNNs). To overcome the existing
issues, we introduce an innovative convolutional ensemble network approach
named deep autoencoder (DAE) with ResNet101. This method utilizes
convolution-based deep neural networks for the detection of skin cancer. The
ISIC-2018 public data taken from the source is used for experimental results,
which demonstrate remarkable performance with the different in terms of
performance metrics. The methods result in 96.03% of accuracy, 95.40 % of
precision, 96.05% of recall, 0.9576 of F-measure, 0.98 of AUC.