Pemanfaatan Deep Learning untuk Klasifikasi Kanker Kulit Menggunakan Few-shot Learning Berbasis Prototypical Networks dan Backbone EfficientNet-B0
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Wahyu Setianingsih
*
Universitas Mercu Buana Yogyakarta, Yogyakarta,
Indonesia
- Putry Wahyu Setyaningsih Universitas Mercu Buana Yogyakarta, Yogyakarta, Indonesia (*) Corresponding Author
Abstract
The utilization of Artificial Intelligence in the current era of technological development is increasingly popular, especially in the field of health. The increasing number of skin cancer cases globally is of particular concern today. Therefore, a classification model utilizing deep learning was developed to assist in the effective diagnosis process. However, data limitations and imbalances are often an issue in training skin cancer classification models. This research develops a skin cancer classification model using the Few-shot Learning approach with Prototypical Networks architecture and EfficientNet-B0 backbone. The research aims to develop an image-based skin cancer classification model and evaluate how effectively the model performs in classifying various types of skin lesions. Experimental results show that increasing k-shots has a positive impact on model accuracy. The best results were obtained in the 10-shot 15-query scheme with an accuracy value of 86.73% and supported by an ROC AUC value of 94%. This study proves that the few-shot learning approach with Prototypical Networks architecture and EfficientNet-B0 backbone is effective for skin cancer classification under limited dataset conditions. This model also has the potential to be an early diagnosis tool.
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References
M. Y. Khairi, E. A. M. Sampetoding, and Y. S. Pongtambing, “Studi Literatur Penerapan Deep Learning dalam Analisis Citra Medis di Indonesia,” Heal. J. PUBLIC Heal. Perspect. Vol., vol. 01, no. 01, pp. 15–24, 2024, doi: 10.62330/healthsense.v1i1.149.
N. A. Winanti, D. P. Martiyaningsih, C. A. A. Soemedhy, and U. Athiyah, “Analisis Klasifikasi Citra Kanker Kulit dengan Random Forest,” Remik, vol. 7, no. 1, pp. 506–515, 2023, doi: 10.33395/remik.v7i1.12102.
Q. F. Yeo, S. Y. Ooi, Y. H. Pang, and Y. H. Gan, “Facial Skin Type Analysis Using Few-shot Learning with Prototypical Networks,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 6, pp. 2249–2266, 2023, doi: 10.18517/ijaseit.13.6.19040.
Q. A. Fitroh and S. ’Uyun, “Deep Transfer Learning to Improve Classification Accuracy in Dermoscopic Images of Skin Cancer,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 12, no. 2, pp. 78–84, 2022, doi: 10.22146/jnteti.v12i2.6502.
Sofia Saidah, I. P. Y. N. Suparta, and E. Suhartono, “Modifikasi Convolutional Neural Network Arsitektur GoogLeNet dengan Dull Razor Filtering untuk Klasifikasi Kanker Kulit,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 11, no. 2, pp. 148–153, 2022, doi: 10.22146/jnteti.v11i2.2739.
O. Orlando and M. E. Al Rivan, “Klasifikasi Jenis kanker Kulit Manusia Menggunakan Convolution Neural Network,” MDP Student Conf., vol. 2, no. 1, pp. 144–150, 2023, doi: 10.35957/mdp-sc.v2i1.4335.
M. R. Ashari, Z. Sari, and D. Rizki, “Klasifikasi Kanker Kulit Menggunakan Metode Deep Learning,” J. Repos., vol. 6, no. 1, pp. 11–16, 2024, doi: 10.22219/repositor.v6i1.29358.
R. Yohannes and M. E. Al Rivan, “Klasifikasi Jenis Kanker Kulit Menggunakan CNN-SVM,” J. Algoritm., vol. 2, no. 2, pp. 133–144, 2022, doi: 10.35957/algoritme.v2i2.2363.
G. P. H. P. Gusti, E. Haerani, F. Syafria, F. Yanto, and S. K. Gusti, “Implementasi Algoritma Convolutional Neural Network (Resnet-50) untuk Klasifikasi Kanker Kulit Benign dan Malignant,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 3, pp. 984–992, 2024, doi: 10.57152/malcom.v4i3.1398.
F. Y. Permana, C. Sri, K. Aditya, and D. R. Chandranegara, “Segmentasi dan Klasifikasi Gambar Citra pada Kanker Kulit Menggunakan Metode Convolutional Neural Network ( CNN ) dengan Arsitektur ResNet-50,” J. Repos., vol. 6, no. 4, pp. 391–404, 2024, doi: 10.22219/repositor.v6i4.
S. Chamarthi, K. Fogelberg, J. Gawlikowski, and T. J. Brinker, “Few-shot learning for skin lesion classification: A prototypical networks approach,” Informatics Med. Unlocked, vol. 48, 2024, doi: 10.1016/j.imu.2024.101520.
F. Pahde, M. Puscas, T. Klein, and M. Nabi, “Multimodal prototypical networks for few-shot learning,” Proc. - 2021 IEEE Winter Conf. Appl. Comput. Vision, WACV 2021, pp. 2643–2652, 2021, doi: 10.1109/WACV48630.2021.00269.
S. Laenen and L. Bertinetto, “On Episodes, Prototypical Networks, and Few-Shot Learning,” Adv. Neural Inf. Process. Syst., vol. 29, pp. 24581–24592, 2021, doi: 10.48550/arXiv.2012.09831.
K. Desingu, M. P., and A. Chandrabose, “Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative Embeddings,” arXiv, vol. abs/2210.1, 2022, doi: 10.48550/arXiv.2210.16954.
T. Chen, Q. Liu, and J. Yang, “Few-Shot Classification with Multiscale Feature Fusion for Clinical Skin Disease Diagnosis,” Clin. Cosmet. Investig. Dermatol., vol. 17, pp. 1007–1026, 2024, doi: 10.2147/CCID.S458255.
M. A. H. Khan, S. M. Boddepalli, S. Bhattacharyya, and D. Mitra, “Few-Shot Classification and Anatomical Localization of Tissues in SPECT Imaging,” IEEE Trans. Nucl. Sci., 2025, doi: 10.48550/arXiv.2502.06632.
H. S. Risfendra, Gheri Febri Ananda, “Deep Learning-Based Waste Classification with Transfer Learning Using EfficientNet-B0 Model,” J. RESTI(Rekayasa Sist. dan Teknol. Informasi), vol. 8, no. 4, pp. 535–541, 2024, doi: 10.29207/resti.v8i4.5875.
K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, “Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer,” Neurosci. Informatics, vol. 2, no. 4, p. 100034, 2022, doi: 10.1016/j.neuri.2021.100034.
Q. H. Trinh, T. H. N. Mau, R. Zosimov, and M. Van Nguyen, “EfficientNet for Brain-Lesion Classification,” Int. MICCAI Brainlesion Work., vol. 12962, pp. 249–260, 2022, doi: 10.1007/978-3-031-08999-2_20.
J. Huang, B. Wu, P. Li, X. Li, and J. Wang, “Few-Shot Learning for Radar Emitter Signal Recognition Based on Improved Prototypical Network,” Remote Sens., vol. 14, no. 7, 2022, doi: 10.3390/rs14071681.
A. Eryana, S. Firmansyah, T. Informatika, I. I. B. Darmajaya, B. Lampung, and D. A. Tenggara, “Prediksi Malaria Menggunakan Metode Pre-Trained Model Algoritma EfficientNet-B0 dan MobileNet-V2,” J. Ilm. Komputasi, vol. 22, no. 1, pp. 71–80, 2023, doi: 10.32409/jikstik.22.1.3332.
Luqman Hakim, Z. Sari, and H. Handhajani, “Klasifikasi Citra Pigmen Kanker Kulit Menggunakan Convolutional Neural Network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 379–385, 2021, doi: 10.29207/resti.v5i2.3001.
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