Investigating The Effectiveness of Various Convolutional Neural Network Model Architectures for Skin Cancer Melanoma Classification
DOI:
https://doi.org/10.30812/matrik.v23i1.3185Keywords:
Melanoma, Skin Cancer, Deep Learning, Convolutional Neural NetworkAbstract
Melanoma is one of the most dangerous types of skin cancer. Since 2018, the number of skin cancer cases in the US has increased and exceeded 100,000. Melanoma is the third most common cancer in Indonesia, following womb cancer and breast cancer. Standard detection of melanoma skin cancer biopsy is costly and time-consuming. The purpose of this research is to build and compare melanoma skin cancer detection using various Convolutional Neural Network method. This research used four CNN model architectures methods, VGG-16, LeNet, Xception, and MobileNet. The dataset for this research is image data that consists of 9605 data divided into benign and malignant classes. The data will be augmented to increase its quantity. After that, the data will be trained using four CNN architecture models and evaluated using the confusion matrix. The result of this study is that Xception model has the best accuracy and the lowest loss, with 93% accuracy and 19% loss, with precision 93%, recall 93,5%, and f1-score 93%. Whereas the other model, VGG-16 gives 90 % accuracy, 27% loss, LeNet 89,7% accuracy, 28% loss, and mobileNet 90,8% accuracy and 22,5% loss.
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References
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[2] Z. Khazaei, F. Ghorat, A. M. Jarrahi, H. A. Adineh, M. Sohrabivafa, and E. Goodarzi, “Global incidence and mortality of skin cancer by histological subtype and its relationship with the human development index (HDI); an ecology study in 2018,†World Cancer Res. J., vol. 6, pp. 1–14, 2019, doi: 10.32113/wcrj_20194_1265.
[3] 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, Apr. 2021, doi: 10.29207/resti.v5i2.3001.
[4] N. Alyyu, R. Fuadah, and N. Pratiwi, “Klasifikasi Kanker Kulit Ganas Dan Jinak Menggunakan Metode Convolutional Neural Network,†in e-Proceeding of Engineering, Dec. 2022, vol. 8, pp. 3200–3206.
[5] S. Sa’idah, I. Putu, Y. Nugraha 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, May 2022, doi: https://doi.org/10.22146/jnteti.v11i2.2739.
[6] R. Agustina, R. Magdalena, and N. K. C. Pratiwi, “Klasifikasi Kanker Kulit menggunakan Metode Convolutional Neural Network dengan Arsitektur VGG-16,†ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 2, p. 446, Apr. 2022, doi: 10.26760/elkomika.v10i2.446.
[7] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,†IEEE Trans. Neural Networks Learn. Syst., vol. 33, no. 12, pp. 6999–7019, Dec. 2022, doi: 10.1109/TNNLS.2021.3084827.
[8] P. Adi Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network ( CNN ) pada Ekspresi Manusia,†J. ALGOR, vol. 2, no. 1, pp. 12–21, 2020.
[9] Z. Niswati, R. Hardatin, M. N. Muslimah, and S. N. Hasanah, “Perbandingan Arsitektur ResNet50 dan ResNet101 dalam Klasifikasi Kanker Serviks pada Citra Pap Smear,†Fakt. Exacta, vol. 14, no. 3, pp. 160–167, Oct. 2021, doi: 10.30998/faktorexacta.v14i3.10010.
[10] E. Yilmaz and M. Trocan, “A modified version of GoogLeNet for melanoma diagnosis,†J. Inf. Telecommun., vol. 5, no. 3, pp. 395–405, 2021, doi: 10.1080/24751839.2021.1893495.
[11] T. R. Savera, W. H. Suryawan, and A. W. Setiawan, “Deteksi Dini Kanker Kulit menggunakan K-NN dan Convolutional Neural Network,†J. Teknol. Inf. Dan Ilmu Komput., vol. 7, no. 2, pp. 373–378, 2020, doi: 10.25126/jtiik.202072602.
[12] R. Yohannes and M. Rivan, “Klasifikasi Jenis Kanker Kulit Menggunakan CNN-SVM,†J. Algoritm., vol. 2, no. 2, pp. 133–144, Apr. 2022.
[13] S. Bechelli and J. Delhommelle, “Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images,†Bioengineering, vol. 9, no. 3, pp. 1–18, Mar. 2022, doi: 10.3390/bioengineering9030097.
[14] R. Refianti, A. B. Mutiara, and R. P. Priyandini, “Classification of Melanoma Skin Cancer using Convolutional Neural Network,†Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 3, pp. 409–417, 2019, doi: https://dx.doi.org/10.14569/IJACSA.2019.0100353.
[15] V. Radhika and B. S. Chandana, “Skin Melanoma Classification from Dermoscopy Images using ANU-Net Technique,†Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 10, pp. 928–938, 2022, doi: https://dx.doi.org/10.14569/IJACSA.2022.01310109.
[16] A. Wibowo, C. A. Hartanto, and P. W. Wirawan, “Android skin cancer detection and classification based on mobilenet v2 model,†Int. J. Adv. Intell. Informatics, vol. 6, no. 2, pp. 135–148, Jul. 2020, doi: 10.26555/ijain.v6i2.492.
[17] M. N. Qureshi, M. S. Umar, and S. Shahab, “A Transfer-Learning-Based Novel Convolution Neural Network for Melanoma Classification,†Computers, vol. 11, no. 5, pp. 1–18, Apr. 2022, doi: 10.3390/computers11050064.
[18] Q. Aina Fitroh and S. ’Uyun, “Deep Transfer Learning untuk Meningkatkan Akurasi Klasifikasi pada Citra Dermoskopi Kanker Kulit,†J. Nasionalteknik Elektro dan Teknol. Inf., vol. 12, no. 2, pp. 78–84, May 2023, doi: https://doi.org/10.22146/jnteti.v12i2.6502.
[19] A. Ajit, K. Acharya, and A. Samanta, “A Review of Convolutional Neural Networks,†in International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020, Feb. 2020, pp. 1–5. doi: 10.1109/ic-ETITE47903.2020.049.
[20] A. Victor Ikechukwu, S. Murali, R. Deepu, and R. C. Shivamurthy, “ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images,†Glob. Transitions Proc., vol. 2, no. 2, pp. 375–381, Nov. 2021, doi: 10.1016/j.gltp.2021.08.027.
[21] M. Ezar Al Rivan and Orlando, “Klasifikasi Jenis Kanker Kulit Manusia Menggunakan Convolutional Neural Network,†in 2nd MDP Student Conference 2023, 2023, pp. 144–150.
[22] O. Rochmawanti, F. Utaminingrum, and F. A. Bachtiar, “Analisis Performa Pre-Trained Model Convolutional Neural Network dalam Mendeteksi Penyakit Tuberkulosis,†J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 4, pp. 805–8013, 2021, doi: 10.25126/jtiik.202184441.
[23] M. Rahimzadeh and A. Attar, “A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2,†Informatics Med. Unlocked, vol. 19, p. 100360, Jan. 2020, doi: 10.1016/j.imu.2020.100360.
[24] M. Sandler, A. Howard, M. Zhu, and A. Zhmoginov, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,†in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510–4520. doi: 10.48550/arXiv.1801.04381.
[25] W. Sae-Lim, W. Wettayaprasit, and P. Aiyarak, “Convolutional Neural Networks Using MobileNet for Skin Lesion Classification,†in Artificial Intelligence Research Lab, Department of Computer Sciencence of Songkla University, Jul. 2019, pp. 242–247.
[26] Y. Zhou, S. Chen, Y. Wang, and W. Huan, “Review of research on lightweight convolutional neural networks,†in IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC 2020), 2020, pp. 1713–1720.
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2023-10-07
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[1]
R. H. Jatmiko and Y. Pristyanto, “Investigating The Effectiveness of Various Convolutional Neural Network Model Architectures for Skin Cancer Melanoma Classification”, MATRIK, vol. 23, no. 1, pp. 1–16, Oct. 2023, doi: 10.30812/matrik.v23i1.3185.
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