Skin Cancer Cell Detection using Image Processing
##plugins.themes.academic_pro.article.sidebar##
##plugins.themes.academic_pro.article.main##
Abstract
Early diagnosis and precise detection of skin cancer represent a global health priority since this disease remains highly dangerous while being among the most frequent ones. This research investigates the effectiveness of deep learning techniques, specifically Convolutional Neural Networks (CNN) and the VGG16 architecture, for skin cancer detection and classification. The study works with images from the International Skin Imaging Collaboration (ISIC) while employing resizing and augmentation preprocessing to boost its model performance. We evaluate the proposed model using precision, recall, and F1-score metrics to ensure accurate classification. The proposed CNN model achieved 87% validation accuracy, outperforming the VGG16 model, which attained 65% accuracy. Experimental results highlight the potential of AI-driven models in improving diagnostic accuracy, demonstrating their significance in medical image analysis and early skin cancer detection.
##plugins.themes.academic_pro.article.details##
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
- Haque, T., Rahman, K. M., Thurston, D. E., Hadgraft, J., & Lane, M. E. (2015). Topical therapies for skin cancer and actinic keratosis. European Journal of Pharmaceutical Sciences.
- Naves, L. B., Dhand, C., Venugopal, J. R., Rajamani, L., Ramakrishna, S., & Almeida, L. (2017). Nanotechnology for the treatment of melanoma skin cancer. Progress in Biomaterials.
- Karadag, A. S., & Parish, L. C. (2018). The status of the seborrheic keratosis. Clinics in Dermatology, 36(2).
- Bath-Hextall, F., Bong, J., Perkins, W., & Williams, H. (2004). Interventions for basal cell carcinoma of the skin: Systematic review. British Medical Journal.
- Dika, E., Scarfì, F., Ferracin, M., Broseghini, E., Marcelli, E., Bortolani, B., … Lambertini, M. (2020). Basal cell carcinoma: A comprehensive review. International Journal of Molecular Sciences.
- Clayman, G. L., Lee, J. J., Holsinger, F. C., Zhou, X., Duvic, M., El-Naggar, A. K., … Lippman, S. M. (2005). Mortality risk from squamous cell skin cancer. Journal of Clinical Oncology, 23(4).
- Alves, J. V. P., Matos, D. M., Barreiros, H. F., & Bártolo, E. A. F. L. F. (2014). Variants of dermatofibroma - A histopathological study. Anais Brasileiros de Dermatologia, 89(3).
- Rezaoana, N., Hossain, M. S., & Andersson, K. (2020). Detection and Classification of Skin Cancer by Using a Parallel CNN Model. In Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020.
- M, V. M. (2019). Melanoma Skin Cancer Detection using Image Processing and Machine Learning. International Journal of Trend in Scientific Research and Development, Volume-3(Issue-4).
- Zghal, N. S., & Derbel, N. (2018). Melanoma Skin Cancer Detection based on Image Processing. Current Medical Imaging Formerly Current Medical Imaging Reviews, 16(1).
- Taye, M. M. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. Computation.
- LeCun, Y., Hinton, G., & Bengio, Y. (2015). Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton. Nature, 521.
- ISIC Challenge ISIC-Archive.com . (n.d.). Retrieved May 26, 2025, from https://challenge.isic- archive.com/data/.