@article{Zareen2024, 
author = {Syeda Shamaila Zareen and Guangmin Sun and Mahwish Kundi and Syed Furqan Qadri and Salman Qadri},
title = {Enhancing Skin Cancer Diagnosis with Deep Learning: A Hybrid CNN-RNN Approach},
year = {2024},
journal = {Computers, Materials & Continua},
volume = {79},
number = {1},
pages = {1497-1519},
keywords = {Skin cancer classification, deep learning, Convolutional Neural Network (CNN), RNN, ResNet-50},
url = {https://www.sciopen.com/article/10.32604/cmc.2024.047418},
doi = {10.32604/cmc.2024.047418},
abstract = {Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassing previous methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscores the model’s competence in categorizing skin cancer types. This research contributes a sophisticated model and valuable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporal complexities, offering a sophisticated solution for dermatological diagnostics research.}
}