Skin cancer remains a significant global health challenge,and early detection is crucial to improving patient outcomes.This study presents a novel deep learning framework that combines Convolutional Neural Networks(CN...Skin cancer remains a significant global health challenge,and early detection is crucial to improving patient outcomes.This study presents a novel deep learning framework that combines Convolutional Neural Networks(CNNs),Transformers,and Gated Recurrent Units(GRUs)for robust skin cancer classification.To address data set imbalance,we employ StyleGAN3-based synthetic data augmentation alongside traditional techniques.The hybrid architecture effectively captures both local and global dependencies in dermoscopic images,while the GRU component models sequential patterns.Evaluated on the HAM10000 dataset,the proposed model achieves an accuracy of 90.61%,outperforming baseline architectures such as VGG16 and ResNet.Our system also demonstrates superior precision(91.11%),recall(95.28%),and AUC(0.97),highlighting its potential as a reliable diagnostic tool for the detection of melanoma.This work advances automated skin cancer diagnosis by addressing critical challenges related to class imbalance and limited generalization in medical imaging.展开更多
文摘Skin cancer remains a significant global health challenge,and early detection is crucial to improving patient outcomes.This study presents a novel deep learning framework that combines Convolutional Neural Networks(CNNs),Transformers,and Gated Recurrent Units(GRUs)for robust skin cancer classification.To address data set imbalance,we employ StyleGAN3-based synthetic data augmentation alongside traditional techniques.The hybrid architecture effectively captures both local and global dependencies in dermoscopic images,while the GRU component models sequential patterns.Evaluated on the HAM10000 dataset,the proposed model achieves an accuracy of 90.61%,outperforming baseline architectures such as VGG16 and ResNet.Our system also demonstrates superior precision(91.11%),recall(95.28%),and AUC(0.97),highlighting its potential as a reliable diagnostic tool for the detection of melanoma.This work advances automated skin cancer diagnosis by addressing critical challenges related to class imbalance and limited generalization in medical imaging.
