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Enhancing Skin Cancer Diagnosis with Deep Learning: A Hybrid CNN-RNN Approach

Syeda Shamaila Zareen1,*, Guangmin Sun1,*, Mahwish Kundi2, Syed Furqan Qadri3, Salman Qadri4

1 Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
2 Computer Science International Engineering Collage, Maynooth University, Kildare, W23 F2H6, Irland
3 Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou, 311121, China
4 Computer Science Department, MNS University of Agriculture, Multan, 59220, Pakistan

* Corresponding Authors: Syeda Shamaila Zareen. Email: email; Guangmin Sun. Email: email

(This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)

Computers, Materials & Continua 2024, 79(1), 1497-1519. https://doi.org/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.

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APA Style
Zareen, S.S., Sun, G., Kundi, M., Qadri, S.F., Qadri, S. (2024). Enhancing skin cancer diagnosis with deep learning: A hybrid CNN-RNN approach. Computers, Materials & Continua, 79(1), 1497-1519. https://doi.org/10.32604/cmc.2024.047418
Vancouver Style
Zareen SS, Sun G, Kundi M, Qadri SF, Qadri S. Enhancing skin cancer diagnosis with deep learning: A hybrid CNN-RNN approach. Comput Mater Contin. 2024;79(1):1497-1519 https://doi.org/10.32604/cmc.2024.047418
IEEE Style
S.S. Zareen, G. Sun, M. Kundi, S.F. Qadri, and S. Qadri "Enhancing Skin Cancer Diagnosis with Deep Learning: A Hybrid CNN-RNN Approach," Comput. Mater. Contin., vol. 79, no. 1, pp. 1497-1519. 2024. https://doi.org/10.32604/cmc.2024.047418



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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