TY  - EJOU
AU  - Varghese, Alex 
AU  - Jain, Achin 
AU  - Rahman, Mohammed Inamur 
AU  - Khan, Mudassir 
AU  - Dubey, Arun Kumar 
AU  - Ahmad, Iqrar 
AU  - Narayan, Yash Prakash 
AU  - Panwar, Arvind 
AU  - Choubey, Anurag 
AU  - Mallik, Saurav 

TI  - Robust Skin Cancer Detection through CNN-Transformer-GRU Fusion and Generative Adversarial Network Based Data Augmentation
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 2
SN  - 1526-1506

AB  - 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.
KW  - Skin cancer detection; deep learning; CNN; transformer; GRU; StyleGAN3

DO  - 10.32604/cmes.2025.067999