ADVANCED SKIN CANCER DETECTION USING CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING

Year 2024, Volume: 10 Issue: 2, 167 - 178, 30.12.2024

Emrah Aslan , Yıldırım Özüpak

https://doi.org/10.51477/mejs.1592302

Abstract

This study investigates the effectiveness of MobileNetV2 transfer learning method and a deep learning based Convolutional Neural Network (CNN) model in the categorization of malignant and benign skin lesions in skin cancer diagnosis. Since skin cancer is a disease that can be cured with early detection but can be fatal if delayed, accurate diagnosis is of great importance. The model was trained with MobileNetV2 architecture and performed the classification task with high accuracy on images of skin lesions. Metrics such as accuracy, recall, precision and F1 score obtained during the training and validation processes support the high performance of the model. The accuracy of the model was 92.97%, Recall 92.71%, Precision 94.70% and F1 score 93.47%. The results show that the CNN-based MobileNetV2 model is a reliable and effective tool for skin cancer diagnosis, but small fluctuations in the validation phase require further data and hyperparameter optimization to further improve the generalization ability of the model. This study demonstrates that CNN-based models enhanced with MobileNetV2 transfer learning offer a powerful solution to medical image classification problems and have the potential to contribute to the development of early detection systems in the healthcare field.

Keywords

Skin cancer, CNN, Transfer learning, Classification, MobileNetV2

References

• Naqvi, M., Gilani, S. Q., Syed, T., Marques, O., Kim, H., “Skin Cancer Detection Using Deep Learning-A Review”, Diagnostics, 13(11), 1911, 2022. doi: 10.3390/diagnostics13111911.
• Vidya, M., Karki, M. V., “Skin Cancer Detection using Machine Learning Techniques”, in: Proc. IEEE Int. Conf. Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2020, pp. 1-5. doi: 10.1109/CONECCT50063.2020.9198489.
• Daghrir, J., Tlig, L., Bouchouicha, M., Sayadi, M., “Melanoma Skin Cancer Detection Using Deep Learning and Classical Machine Learning Techniques: A Hybrid Approach”, in: Proc. 5th Int. Conf. Advanced Technologies for Signal and Image Processing (ATSIP), Sousse, Tunisia, 2020, pp. 1-5. doi: 10.1109/ATSIP49331.2020.9231544.
• Bayram, M., Arserim, M. A., "Analysis of Epileptic iEEG Data by Applying Convolutional Neural Networks to Low-Frequency Scalograms," in IEEE Access, vol. 9, pp. 162520-162529, 2021. doi: 10.1109/ACCESS.2021.3132128.
• Murugan, A., Nair, S. A. H., Peace Preethi, A. A., Sanal Kumar, K. P., “Diagnosis of Skin Cancer Using Machine Learning Techniques”, Microprocessors and Microsystems, 81, 103727, 2021. doi: 10.1016/j.micpro.2020.103727.
• Luu, T. N., Phan, Q. H., Le, T. H., Pham, T. T. H., “Classification of Human Skin Cancer Using Stokes-Mueller Decomposition Method and Artificial Intelligence Models”, Optik, 249, 168239, 2022. doi: 10.1016/j.ijleo.2021.168239.
• Tembhurne, J. V., Hebbar, N., Patil, H. Y., et al., “Skin Cancer Detection Using Ensemble of Machine Learning and Deep Learning Techniques”, Multimedia Tools and Applications, 82, 27501–27524, 2023. doi: 10.1007/s11042-023-14697-3.
• Monika, M. K., Vignesh, N. A., Kumari, C. U., Kumar, M. N. V. S. S., Lydia, E. L., “Skin Cancer Detection and Classification Using Machine Learning”, Materials Today: Proceedings, 33(7), 4266-4270, 2020. doi: 10.1016/j.matpr.2020.07.366.
• Nancy, N. V. A. O., Prabhavathy, P., Arya, M. S., et al., “Comparative Study and Analysis on Skin Cancer Detection Using Machine Learning and Deep Learning Algorithms”, Multimedia Tools and Applications, 82, 45913–45957, 2023. doi: 10.1007/s11042-023-16422-6.
• Mazhar, T., Haq, I., Ditta, A., Mohsan, S. A. H., Rehman, F., Zafar, I., Gansau, J. A., Goh, L. P. W., “The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer”, Healthcare, 11, 415, 2023. doi: 10.3390/healthcare11030415.
• Gomathi, E., Jayasheela, M., Thamarai, M., Geetha, M., “Skin Cancer Detection Using Dual Optimization-Based Deep Learning Network”, Biomedical Signal Processing and Control, 84, 104968, 2023. doi: 10.1016/j.bspc.2023.104968.
• Ghosh, H., Rahat, I. S., Mohanty, S. N., Ravindra, J. V. R., Sobur, A., “A Study on the Application of Machine Learning and Deep Learning Techniques for Skin Cancer Detection”, Zenodo, 2024. doi: 10.5281/zenodo.10525954.
• Priyadharshini, N., Selvanathan, S. N., Hemalatha, B., Sureshkumar, C., “A Novel Hybrid Extreme Learning Machine and Teaching–Learning-Based Optimization Algorithm for Skin Cancer Detection”, Healthcare Analytics, 3, 100161, 2023. doi: 10.1016/j.health.2023.100161.
• Balaha, H. M., Hassan, A. E. S., “Skin Cancer Diagnosis Based on Deep Transfer Learning and Sparrow Search Algorithm”, Neural Computing and Applications, 35, 815–853, 2023. doi: 10.1007/s00521-022-07762-9.
• Shah, A., Shah, M., Pandya, A., Sushra, R., Mehta, M., Patel, K., “A Comprehensive Study on Skin Cancer Detection Using Artificial Neural Network (ANN) and Convolutional Neural Network (CNN)”, Clinical eHealth, 6, 76-84, 2023. doi: 10.1016/j.ceh.2023.08.002.
• Pacal, I., Alaftekin, M., Zengul, F. D., “Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-Head Self-Attention and SwiGLU-Based MLP”, J Digit Imaging. Inform. Med., 37, 3174–3192, 2024. doi: 10.1007/s10278-024-01140-8.
• Kaggle, “Skin Cancer: Malignant vs Benign”, [Online], Available: https://www.kaggle.com/datasets/fanconic/skin-cancer-malignant-vs-benign/data.
• Erkan, E., Arserim, M. A., "Mobile Robot Application with Hierarchical Start Position DQN," in Computational Intelligence and Neuroscience, vol. 2022, Article ID 4115767, 21 pages, 2022. doi: 10.1155/2022/4115767.
• Pacal, I., “Improved Vision Transformer with Lion Optimizer for Lung Diseases Detection”, International Journal of Engineering Research and Development, 16(2), 760-776, 2024. doi: 10.29137/umagd.1469472.
• Mahbod, M. A., Schaefer, G., Wang, C., Ecker, R., Ellinge, I., “Skin Lesion Classification Using Hybrid Deep Neural Networks”, in: Proc. ICASSP 2019–IEEE Int. Conf. Acoustics, Speech and Signal Processing, Brighton, UK, May 12–17, 2019, pp. 1229–1233.
• DeVries, T., Ramachandram, D., “Skin Lesion Classification Using Deep Multi-Scale Convolutional Neural Networks”, arXiv, 2017. Available: http://arxiv.org/abs/1703.01402.
• Kousis, I., Perikos, I., Hatzilygeroudis, I., Virvou, M., “Deep Learning Methods for Accurate Skin Cancer Recognition and Mobile Application”, Electronics, 11(9), 1294, 2022. doi: 10.3390/electronics11091294.
• Pacal, I., “A Novel Swin Transformer Approach Utilizing Residual Multi-Layer Perceptron for Diagnosing Brain Tumors in MRI Images”, Int. J. Mach. Learn. & Cyber., 15, 3579–3597, 2024. doi: 10.1007/s13042-024-02110-w.