ResNet101 and DAE for Enhance Quality and Classification Accuracy in Skin Cancer Imaging

arXiv:2403.14248v1 Announce Type: cross
Abstract: 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 …

abstract accuracy advanced arxiv cancer challenges classification cnns computer computer vision convolutional neural networks cs.cv detection eess.iv face features gap health imaging issue networks neural networks quality skin cancer survival type vision