@Article{cmc.2024.047418,
AUTHOR = {Syeda Shamaila Zareen, Guangmin Sun, Mahwish Kundi, Syed Furqan Qadri, Salman Qadri},
TITLE = {Enhancing Skin Cancer Diagnosis with Deep Learning: A Hybrid CNN-RNN Approach},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {79},
YEAR = {2024},
NUMBER = {1},
PAGES = {1497--1519},
URL = {http://www.techscience.com/cmc/v79n1/56271},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2024.047418}
}