Comparison of CNN and SVM Methods on Web-based Skin Disease Classification Process


  • Ahmad Ilham Kushartanto Universitas Nasional
  • Fauziah Faculty of Communication and Information Technology, Universitas Nasional
  • Rima Tamara Aldisa Faculty of Communication and Information Technology, Universitas Nasional




Classification, Convolutional Neural Network, Image, Support Vector Machine, Web Application


Skin, as the outermost layer of the body, is often in contact with bacteria, germs and viruses because of its most external position. According to statistics from the 2009 Indonesian Health Profile, skin illness is the third most common ailment seen in outpatient settings across the country's hospitals. Therefore, maintaining healthy skin is important because it protects the body's internal organs from injury and attack by pathogens. The development of image classification, such as the classification of skin diseases, has become a focus in the health sector. This research analyses the performance of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in web-based skin disease classification and overcomes the problem of imbalanced training data. With data augmentation and preprocess, this research improves data generalization and compares performance metrics such as Recall, Accuracy, and F1 Score. The results show that the average accuracy of CNN is 83.8%, while SVM reaches 81%. Although both models have high metrics for the normal class, other more complicated classes can only be handled by CNN with a value of more than 0.9. Apart from that, the CNN method also provides a higher Confidence Score than SVM, as well as a faster execution time. In conclusion, the CNN method is superior and recommended for skin disease classification based on web applications based on various performance test results.

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Akyeramfo-Sam, S., Addo Philip, A., Yeboah, D., Nartey, N. C., & Kofi Nti, I. (2019). A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks. International Journal of Information Technology and Computer Science, 11(11), 54–60.

Alkolifi Alenezi, N. S. (2019). A Method of Skin Disease Detection Using Image Processing and Machine Learning. Procedia Computer Science, 163, 85–92.

Bhadula*, S., Sharma, S., Juyal, P., & Kulshrestha, C. (2019). Machine Learning Algorithms based Skin Disease Detection. International Journal of Innovative Technology and Exploring Engineering, 9(2), 4044–4049.

Brinker, T. J., Hekler, A., Enk, A. H., Berking, C., Haferkamp, S., Hauschild, A., Weichenthal, M., Klode, J., Schadendorf, D., Holland-Letz, T., von Kalle, C., Fröhling, S., Schilling, B., & Utikal, J. S. (2019). Deep neural networks are superior to dermatologists in melanoma image classification. European Journal of Cancer, 119, 11–17.

Brinker, T. J., Hekler, A., Enk, A. H., Klode, J., Hauschild, A., Berking, C., Schilling, B., Haferkamp, S., Schadendorf, D., Holland-Letz, T., Utikal, J. S., von Kalle, C., Ludwig-Peitsch, W., Sirokay, J., Heinzerling, L., Albrecht, M., Baratella, K., Bischof, L., Chorti, E., … Schrüfer, P. (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. European Journal of Cancer, 113, 47–54.

Kumar, N. V., Kumar, P. V., Pramodh, K., & Karuna, Y. (2019). Classification of Skin diseases using Image processing and SVM. Proceedings - International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019, 1–5.

Lu, J., Tong, X., Wu, H., Liu, Y., Ouyang, H., & Zeng, Q. (2023). Image classification and auxiliary diagnosis system for hyperpigmented skin diseases based on deep learning. Heliyon, 9(9), e20186.

Magdy, A., Hussein, H., Abdel-Kader, R. F., & Salam, K. A. El. (2023). Performance Enhancement of Skin Cancer Classification Using Computer Vision. IEEE Access, 11(July), 72120–72133.

Purnama, I. K. E., Hernanda, A. K., Ratna, A. A. P., Nurtanio, I., Hidayati, A. N., Purnomo, M. H., Nugroho, S. M. S., & Rachmadi, R. F. (2019). Disease Classification based on Dermoscopic Skin Images Using Convolutional Neural Network in Teledermatology System. 2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2019 - Proceeding, 2019-Novem, 1–5.

Roslan, R., Razly, I. N. M., Sabri, N., & Ibrahim, Z. (2020). Evaluation of psoriasis skin disease classification using convolutional neural network. IAES International Journal of Artificial Intelligence, 9(2), 349–355.

Saifan, R., & Jubair, F. (2022). Six skin diseases classification using deep convolutional neural network. International Journal of Electrical and Computer Engineering, 12(3), 3072–3082.

Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308–6325.

Shin, Y., Kim, M., Pak, K. W., & Kim, D. (2020). Practical methods of image data preprocessing for enhancing the performance of deep learning based road crack detection. ICIC Express Letters, Part B: Applications, 11(4), 373–379.

Srinivasu, P. N., Sivasai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021). Networks with MobileNet V2 and LSTM. 1–27.

Tasnim, Z., Chakraborty, S., Shamrat, F. M. J. M., Chowdhury, A. N., Nuha, H. A., Karim, A., Zahir, S. B., & Billah, M. M. (2021). Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification. International Journal of Advanced Computer Science and Applications, 12(8), 687–696.

Verma, A. K., Pal, S., & Kumar, S. (2019). Classification of skin disease using ensemble data mining techniques. Asian Pacific Journal of Cancer Prevention, 20(6), 1887–1894.

Wu, Z., Zhao, S., Peng, Y., He, X., Zhao, X., Huang, K., Wu, X., Fan, W., Li, F., Chen, M., Li, J., Huang, W., Chen, X., & Li, Y. (2019). Studies on Different CNN Algorithms for Face Skin Disease Classification Based on Clinical Images. IEEE Access, 7, 66505–66511.

Yu, C., Han, R., Song, M., Liu, C., & Chang, C. I. (2020). A simplified 2D-3D CNN architecture for hyperspectral image classification based on spatial-spectral fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 2485–2501.


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How to Cite

Kushartanto, A. I., Fauziah, & Aldisa , R. T. . (2024). Comparison of CNN and SVM Methods on Web-based Skin Disease Classification Process. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 778-788.