Performance Comparison of VGG16, MobileNetV2, and InceptionV3 Convolutional Neural Networks in Classifying Facial Dermatological Conditions

Fadilah Karamun Nisaa Nadiyah; Nayla Nur Alifah; Sri Nurdiati; Elis Khatizah; Mohamad Khoirun Najib

doi:10.37905/jjom.v7i2.33082

Performance Comparison of VGG16, MobileNetV2, and InceptionV3 Convolutional Neural Networks in Classifying Facial Dermatological Conditions

Fadilah Karamun Nisaa Nadiyah, Nayla Nur Alifah, Sri Nurdiati, Elis Khatizah, Mohamad Khoirun Najib

Abstract

This study investigates the performance of three convolutional neural network (CNN) architectures (VGG16, MobileNetV2 and InceptionV3) in classifying two common facial dermatological conditions: acne and dark spots. A dataset of 235 facial skin images was augmented, then used to train and evaluate each model using standard classification metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that MobileNetV2 achieved the highest classification accuracy of 93.13% while maintaining a relatively low computational cost. The model exhibited perfect precision (1.00) for the acne class and a high recall of 0.99 for the dark spots class, indicating its strong capability in accurately and sensitively identifying both lesion types. All three models demonstrated acceptable classification performance for both acne and dark spots classes, as evidenced by their precision, recall, and F1-scores exceeding 70%. This indicates that each model was capable of capturing relevant discriminative features of both lesion types.

Keywords

Classification; CNN; MobilenetV2; VGG16; InceptionV3

Full Text:

PDF

References

Y. S. Bekmirzaev S, Oh S, “Rethnet: Object-by-object learning for detecting facial skin problems,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019, pp. 0–0.

C. Kim, W. Lee, Y. Hong, Y. Han, and M. Jang, “Skin lesion classification using hybrid convolutional neural network with edge, color, and texture information,” Applied Sciences, vol. 13, no. 9, 2023, doi: 10.3390/app13095497.

A. Mahalakshmi, C. Padmavathy, N. Priya, G. Priyanka, and B. Archana, “Skin cancer detection using deep learning,” in 2024 10th International Conference on Electrical Energy Systems (ICEES). IEEE, August 2024, pp. 1–4.

W. Chen, C. Chin, T. Tsai, and M. Chin, “Facial skin image classification system using convolutional neural networks deep learning algorithm,” in 2018 9th International Conference on Awareness Science and Technology (iCAST). IEEE, 2018, pp. 51–55, doi: 10.1109/ICAWST.2018.8517246.

——, “Facial skin image classification system using convolutional neural networks deep learning algorithm,” in 2018 9th International Conference on Awareness Science and Technology (iCAST). IEEE, 2018, pp. 51–55, doi: 10.1109/ICAWST.2018.8517246.

P. Kaur and R. Kumar, “Performance analysis of convolutional neural network architectures over wireless capsule endoscopy dataset,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 1, 2024, doi: 10.11591/eei.v13i1.5858.

R. Klangbunrueang, W. Chansanam, P. Pookduang, and T. Lunrasri, “AI-powered lung cancer detection: Assessing vgg16 and cnn architectures for ct scan image classification,” Informatics, vol. 12, no. 1, p. 18, 2025, doi: 10.3390/informatics12010018.

J. Zheng and M. Du, “Study on tomato disease classification based on leaf image recognition based on deep learning technology,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 4, 2023, doi: 10.14569/IJACSA.2023.0140463.

H. Qassim, A. Verma, and D. Feinzimer, “Compressed residual-vgg16 cnn model for big data places image recognition,” in 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, January 2018, pp. 169–175.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510–4520.

S. Noor, M. Saleem, N. Ismat, H. Inam, A. Siddiqui, and H. Minhas, “Eye on the future: Deep learning with mobilenetv2 for early eye disease diagnosis,” in 2024 Global Conference on Wireless and Optical Technologies (GCWOT). IEEE, 2024, pp. 1–7, doi: 10.1109/GCWOT63882.2024.10805707.

W. L. H. L. S. W. Z. X. D. M. Z. S. W. M. . M. H. Dai, M., “Pepper leaf disease recognition based on enhanced lightweight convolutional neural networks,” Frontiers in Plant Science, vol. 14, 2023, doi: 10.3389/fpls.2023.1230886.

S. Tantry, T. Lal, and S. Rangaswamy, “Skin disease classification using deep learning,” National Academy Science Letters, 2024, doi: 10.1007/s40009-024-01523-z.

S. Kalal, G. Ubale, O. Kamble, A. Kashyap, S. Mhamane, and S. Pande, “Evaluating the performance of pre-trained cnns for multi-class skin disease detection,” in 2024 Fourth International Conference on Multimedia Processing, Communication & Information Technology (MPCIT). IEEE, 2024, pp. 303–308, doi: 10.1109/MPCIT62449.2024.10892670.

G. Jablonski, “Vgg based unsupervised anomaly detection in multivariate time series,” in Proceedings of the International Conference on Computational Science (ICCS). Springer, 2020, pp. 1287–1296, doi: 10.1007/978-3-030-50936-1_107.

V. Semwal, S. Singh, S. Challa, and N. Dua, “Inception inspired cnn-gru hybrid network for human activity recognition,” Multimedia Tools and Applications, vol. 82, pp. 5369–5403, 2022, doi: 10.1007/s11042-021-11885-x.

C. S. Anumol, “Advancements in cnn architectures for computer vision: a comprehensive review,” in 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS). IEEE, November 2023, pp. 1–7.

R. Jain, P. Nagrath, G. Kataria, V. S. Kaushik, and D. J. Hemanth, “Pneumonia detection in chest x-ray images using convolutional neural networks and transfer learning,” Measurement, vol. 165, p. 108046, 2020, doi: 10.1016/j.measurement.2020.108046.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016, [Online]. Available: http://www.deeplearningbook.org.

J. Tohka and M. van Gils, “Evaluation of machine learning algorithms for health and wellness applications: A tutorial,” Computers in Biology and Medicine, vol. 132, p. 104324, 2021, doi: 10.1016/j.compbiomed.2021.104324.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 2015, [Online]. Available: https://arxiv.org/abs/1409.1556.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” 2017, [Online]. Available: https://arxiv.org/abs/1704.04861.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” 2015, [Online]. Available: https://arxiv.org/abs/1512.00567.

S. Rajaraman, S. K. Antani, M. Poostchi, K. Silamut, M. A. Hossain, R. J. Maude, S. Jaeger, and G. R. Thoma, “Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images,” PeerJ, vol. 6, p. e4568, Apr. 2018, doi: 10.7717/peerj.4568.

N. Ma, X. Zhang, H.-T. Zheng, and J. Sun, “Shufflenet v2: Practical guidelines for efficient cnn architecture design,” 2018, [Online]. Available: https://arxiv.org/abs/1807.11164.

DOI: https://doi.org/10.37905/jjom.v7i2.33082