TY  - EJOU
AU  - Zareen, Syeda Shamaila 
AU  - Sun, Guangmin 
AU  - Kundi, Mahwish 
AU  - Qadri, Syed Furqan 
AU  - Qadri, Salman 

TI  - Enhancing Skin Cancer Diagnosis with Deep Learning: A Hybrid CNN-RNN Approach
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 79
IS  - 1
SN  - 1546-2226

AB  - 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.
KW  - Skin cancer classification; deep learning; Convolutional Neural Network (CNN); RNN; ResNet-50

DO  - 10.32604/cmc.2024.047418