PERBANDINGAN KINERJA DAN EFISIENSI ARSITEKTUR CNN VGG-16 DAN RESNET50 DALAM KANKER KULIT DATASET HAM10000

Richard Leonardo

doi:10.23960/jitet.v14i2.9351

Richard Leonardo
Esa Unggul University

DOI: https://doi.org/10.23960/jitet.v14i2.9351

Keywords CNN, Dataset, HAM10000, Resnet50, VGG16

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Abstract

The primary focus of this research is to compare the capabilities and efficiency of two Convolutional Neural Network (CNN) architectures: ResNet50 and VGG16. This comparison is conducted for the task of classifying skin lesion images using the HAM10000 dataset. This dataset comprises dermatoscopic images representing various types of skin lesions, including melanoma, nevus, and other benign and malignant tumors. Both models were optimized using transfer learning with pretrained weights from ImageNet and trained with identical parameters. The research findings indicate that ResNet50 outperformed VGG16, achieving an accuracy of 80.96% on the testing data, whereas VGG16 only reached 74.85%. While ResNet50 demonstrated superior results in terms of validation and testing accuracy, as well as generalization capability, VGG16 performed better on majority classes. Both models encountered difficulties in recognizing minority classes, such as melanoma and dermatofibroma. This challenge is likely attributable to the imbalance in the number of samples across different classes. Although ResNet50 showed overall higher accuracy, this study also highlights the necessity for further approaches, such as data balancing and augmentation, to enhance performance on minority classes. This system holds significant potential to serve as a foundation for developing AI-based skin cancer detection applications, which could assist medical professionals in accelerating diagnoses and improving detection accuracy.

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References

M. Berwick and A. Garcia, “Solar UV Exposure and Mortality from Skin Tumors: An Update BT - Sunlight, Vitamin D and Skin Cancer,” J. Reichrath, Ed., Cham: Springer International Publishing, 2020, pp. 143–154. doi: 10.1007/978-3-030-46227-7_7.

F. J, “The Global Cancer Observatory.” [Online]. Available: https://gco.iarc.who.int/media/globocan/factsheets/populations/360-indonesia-fact-sheet.pdf

“Analisis Perbandingan Kinerja Algoritma Multilayer Perceptron dan K-Nearest Neighbor pada Klasifikasi Tipe Migrain,” J. Teknol. dan Inf., vol. 13, no. 1 SE-Article, pp. 44–55, Feb. 2023.

P. Rakshit, A. Ghosh, C. Chakraborty, J. Paul, and D. Das, “Skin Cancer Detection Using Deep Learning,” Lect. Notes Electr. Eng., vol. 1243 LNEE, pp. 359–372, 2025, doi: 10.1007/978-981-97-6465-5_29.

and T. R. Y. Chen, H. Chen, M. Han, B. Liu, Q. Chen, “A Novel Computing Power Allocation Algorithm for Blockchain System in Multiple Mining Pools Under Withholding Attack,” IEEE Access, vol. 8, pp. 155630–155644, 2020, doi: doi: 10.1109/ACCESS.2020.3017716.

M. T. Z. Quen, J. Mountstephens, Y. G. Teh, and J. Teo, “Medical image interpretation training with a low-cost eye tracking and feedback system: A preliminary study,” Healthc. Technol. Lett., vol. 8, no. 4, pp. 97–103, Aug. 2021, doi: https://doi.org/10.1049/htl2.12014.

A. Mahbod, G. Schaefer, C. Wang, G. Dorffner, R. Ecker, and I. Ellinger, “Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification,” Comput. Methods Programs Biomed., vol. 193, p. 105475, 2020, doi: https://doi.org/10.1016/j.cmpb.2020.105475.

X. Li, X. Zhao, H. Ma, and B. Xie, “Image Analysis and Diagnosis of Skin Diseases - A Review,” Curr. Med. Imaging, vol. 19, no. 3, pp. 199–242, 2023, doi: https://doi.org/10.2174/1573405618666220516114605.

D. Meedeniya, S. De Silva, L. Gamage, and U. Isuranga, “Skin cancer identification utilizing deep learning: A survey,” IET Image Process., vol. 18, no. 13, pp. 3731–3749, 2024, doi: 10.1049/ipr2.13219.

T. Chagovets et al., “Automation of Target Delivery and Diagnostic Systems for High Repetition Rate Laser-Plasma Acceleration,” 2021. doi: 10.3390/app11041680.

R. Alabduljabbar and H. Alshamlan, “Intelligent multiclass skin cancer detection using convolution neural networks,” Comput. Mater. Contin., vol. 69, no. 1, pp. 831–847, 2021, doi: 10.32604/cmc.2021.018402.

and Y. L. X. Zhou, J. Feng, “Non-intrusive load decomposition based on CNN–LSTM hybrid deep learning model,” Energy Reports, vol. 7, pp. 5762–5771, 2021, doi: doi: 10.1016/j.egyr.2021.09.001.

A. Tharwat, “Classification assessment methods,” Appl. Comput. Informatics, vol. 17, no. 1, pp. 168–192, 2021, doi: doi: 10.1016/j.aci.2018.08.003.

T. Chagovets et al., “Automation of Target Delivery and Diagnostic Systems for High Repetition Rate Laser-Plasma Acceleration,” 2021, doi: doi: 10.3390/app11041680.

and B. X. X. Li, X. Zhao, H. Ma, “Image Analysis and Diagnosis of Skin Diseases - A Review,” Curr. Med. Imaging, vol. 19, no. 3, pp. 199–242, 2023, doi: https://doi.org/10.2174/1573405618666220516114605.

A. D. A. N. Sgd, “KLASIFIKASI CITRA PENYAKIT KANKER MULUT MENGGUNAKAN ARSITEKTUR RESNET50 OPTIMASI,” vol. 12, no. 3, 2024.

“Studi Perbandingan Deteksi Intrusi Jaringan Menggunakan Machine Learning: (Metode SVM dan ANN),” J. Teknol. dan Inf, vol. 13, no. 2, pp. 152–164, 2023, doi: doi: 10.34010/jati.v13i2.10484.

U. Ali, I. H. Lee, and M. T. Mahmood, “Guided image filtering in shape-from-focus: A comparative analysis,” Pattern Recognit., vol. 111, p. 107670, 2021, doi: https://doi.org/10.1016/j.patcog.2020.107670.

A. Esteva et al., “A guide to deep learning in healthcare,” Nat. Med., vol. 25, no. 1, pp. 24–29, 2019, doi: 10.1038/s41591-018-0316-z.

S. R. Yang, H. C. Yang, F. R. Shen, and J. Zhao, “Image Data Augmentation for Deep Learning: A Survey,” Ruan Jian Xue Bao/Journal Softw., vol. 36, no. 3, pp. 1390–1412, 2025, doi: 10.13328/j.cnki.jos.007263.

D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl. Soft Comput., vol. 97, no. xxxx, p. 105524, 2020, doi: 10.1016/j.asoc.2019.105524.

S. Sharma and K. Guleria, “A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks,” Procedia Comput. Sci., vol. 218, pp. 357–366, 2022, doi: 10.1016/j.procs.2023.01.018.

A. Tharwat, “Classification assessment methods,” Appl. Comput. Informatics, vol. 17, no. 1, pp. 168–192, Jan. 2021, doi: 10.1016/j.aci.2018.08.003.

X. Zhou, J. Feng, and Y. Li, “Non-intrusive load decomposition based on CNN–LSTM hybrid deep learning model,” Energy Reports, vol. 7, pp. 5762–5771, 2021, doi: 10.1016/j.egyr.2021.09.001.

T. Kattenborn, J. Leitloff, F. Schiefer, and S. Hinz, “Review on Convolutional Neural Networks (CNN) in vegetation remote sensing,” ISPRS J. Photogramm. Remote Sens., vol. 173, pp. 24–49, 2021, doi: https://doi.org/10.1016/j.isprsjprs.2020.12.010.

D. Chicco and G. Jurman, “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,” BMC Genomics, vol. 21, no. 1, p. 6, 2020, doi: 10.1186/s12864-019-6413-7.

“Studi Perbandingan Deteksi Intrusi Jaringan Menggunakan Machine Learning: (Metode SVM dan ANN),” J. Teknol. dan Inf., vol. 13, no. 2 SE-Article, pp. 152–164, Aug. 2023, doi: 10.34010/jati.v13i2.10484.