Classification of Skin Cancer Images with Convolutional Neural Network Architectures

Year 2021, Volume: 16 Issue: 2, 187 - 195, 15.09.2021

Muhammed Yıldırım , Ahmet Çınar

Abstract

The skin, in which our body is completely covered, both provides the heat balance of our body and protects our body against external factors. Skin cancers, which occur as a result of the uncontrolled proliferation of cells on the skin surface, are one of the most common types of cancer in the world. Early detection of skin cancers means early treatment of the disease. With early diagnosis, patients can be cured earlier and mortality rates can be reduced. The hardest part of skin cancer diagnosis is that skin lesions are very similar to each other. Therefore, it is of great importance that skin cancer can be diagnosed and classified as benign or malignant tumor. In this study, Convolutional Neural Network networks are used to determine whether skin cancer is benign or malignant. Separate results are obtained with Alexnet, Resnet50, Densenet201 and Googlenet. Then the performance rates of the models used have been compared. The highest accuracy rate is achieved with the Resnet50 model with 83.49%. This rate is an important value for early diagnosis and treatment of the disease.

Keywords

CNN, Classification, Deep Learning, Image Processing, Skin Cancer

References

• [1] Kabashima, K., Honda, T., Ginhoux, F., & Egawa, G. (2019). The immunological anatomy of the skin. Nature Reviews Immunology, 19(1), 19-30. https://doi.org/10.1038/s41577-018-0084-5
• [2] American Cancer Society: Cancer facts and figures, Aug 2018, [online] Available: https://www.cancer.org/content/dam/cancer-org/research/cancer-factsand-statistics/annual-cancer-facts-and-figures/2018/cancer-facts-andfigures-2018.pdf.
• [3] N. C. F. Codella et al., "Deep learning ensembles for melanoma recognition in dermoscopy images", IBM Journal of Research and Development, vol. 61, pp. 5:1-5:15, 2017.
• [4] Taroni, P., Paganoni, A. M., Ieva, F., Pifferi, A., Quarto, G., Abbate, F., ... & Cubeddu, R. (2017). Non-invasive optical estimate of tissue composition to differentiate malignant from benign breast lesions: A pilot study. Scientific reports, 7(1), 1-11. https://doi.org/10.1038/srep40683.
• [5] Wylie, J. D., Jenkins, P. A., Beckmann, J. T., Peters, C. L., Aoki, S. K., & Maak, T. G. (2018). Computed tomography scans in patients with young adult hip pain carry a lifetime risk of malignancy. Arthroscopy: The Journal of Arthroscopic & Related Surgery, 34(1), 155-163. https://doi.org/10.1016/j.arthro.2017.08.235
• [6] Yap, J., Yolland, W., & Tschandl, P. (2018). Multimodal skin lesion classification using deep learning. Experimental dermatology, 27(11), 1261-1267.https://doi.org/10.1111/exd.13777
• [7] Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010(pp. 177-186). Physica-Verlag HD.
• [8] Deng, L.,& Yu, D. (2014). Deep learning: methods and applications. Foundations and Trends® in Signal Processing, 7(3–4), 197- 387.
• [9] Espinosa, J. E., Velastin, S. A., & Branch, J. W. (2017, November). Vehicle detection using alex net and faster R-CNN deep learning models: a comparative study. In International Visual Informatics Conference (pp. 3-15). Springer, Cham. https://doi.org/10.1007/978-3-319-70010-6_1
• [10] Singh, A. K., Ganapathysubramanian, B., Sarkar, S., & Singh, A. (2018). Deep learning for plant stress phenotyping: trends and future perspectives. Trends in plant science, 23(10), 883-898. https://doi.org/10.1016/j.tplants.2018.07.004
• [11] Yildirim, M., Cinar, A. (2020). A deep learning based hybrid approach for COVID-19 disease detections. Traitement du Signal, 37(3): 461-468. https://doi.org/10.18280/ts.370313.
• [12] Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).
• [13] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
• [14] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
• [15] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
• [16] https://www.kaggle.com/naim99/segmented-images-of-the-skin-cancer-dataset
• [17] Yildirim, M., Çinar, A. (2019). Classification of white blood cells by deep learning methods for diagnosing disease. Revue d'Intelligence Artificielle, Vol. 33, No. 5, pp. 335-340. https://doi.org/10.18280/ria.330502
• [18] Liu, Z. (2020). Soft-shell shrimp recognition based on an improved AlexNet for quality evaluations. Journal of Food Engineering, 266, 109698. https://doi.org/10.1016/j.jfoodeng.2019.109698
• [19] Zahisham, Z., Lee, C. P., & Lim, K. M. (2020, September). Food Recognition with ResNet-50. In 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (pp. 1-5). IEEE. doi: 10.1109/IICAIET49801.2020.9257825.
• [20] Carcagnì, P., Leo, M., Cuna, A., Mazzeo, P. L., Spagnolo, P., Celeste, G., & Distante, C. (2019, September). Classification of skin lesions by combining multilevel learnings in a DenseNet architecture. In International Conference on Image Analysis and Processing (pp. 335-344). Springer, Cham. https://doi.org/10.1007/978-3-030-30642-7_30
• [21] Al-Qizwini, M., Barjasteh, I., Al-Qassab, H., & Radha, H. (2017, June). Deep learning algorithm for autonomous driving using googlenet. In 2017 IEEE Intelligent Vehicles Symposium (IV) (pp. 89-96). IEEE. doi: 10.1109/IVS.2017.7995703.
• [22] Çinar, A., Yıldırım, M. (2020). Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Medical Hypotheses, 139: 109684. https://doi.org/10.1016/j.mehy.2020.109684
• [23] Yildirim, M., Cinar, A. (2020). Classification of Alzheimer's disease MRI images with CNN based hybrid method. Ingénierie des Systèmes d’Information, Vol. 25, No. 4, pp. 413-418. https://doi.org/10.18280/isi.250402