Analysis of Image Preprocessing on EfficientNet-B5 Performance in Acne Severity Classification
DOI:
https://doi.org/10.30865/json.v7i4.9818Keywords:
Acne Classification, Acne Severity, Efficientnet B5, Image Preprocessing, Macro F1-ScoreAbstract
Deep learning based acne severity classification requires consistent color distribution and image illumination for stable feature extraction. Color imbalance, noise, and lighting variations can affect the accuracy and generalization ability of the model, making image preprocessing optimization a crucial aspect in dermatological classification. This study analyzes the impact of image preprocessing on the performance of EfficientNet-B5 in classifying three levels of acne severity using the Kaggle Acne Grading dataset (999 images; 80% training, 20% testing). The experiment compares the default preprocessing (resize, normalization) with the proposed preprocessing: gray-world white balance for color stabilization, bilateral filtering for edge preservation, and adaptive gamma correction for adaptive illumination. The evaluation uses accuracy and loss curves, confusion matrices, and classification reports, focusing on the macro F1-score to assess the balance between precision and recall. The results show a slight increase in accuracy from 77% to 78%, a macro F1 score of 75%, and more controlled overfitting with smaller differences in accuracy and loss between training and validation. Improving image quality before feature extraction contributes to feature representation and balance in multi-class classification.
References
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[32] R. S. Pradini, W. T. Kusuma, and A. S. Budi, “Convolutional neural network model for fingerprint-based gender classification using original and degraded images,” Int. J. Adv. Appl. Sci., vol. 14, no. 4, pp. 1350–1358, 2025, doi: 10.11591/ijaas.v14.i4.pp1350-1358.
[33] A. P. Singhapathiranage, D. D. K. Kelaart, A. A. M. Thathsarani, C. D. L. Liyanage, and B. N. S. Lankasena, “Optimizing Acne Severity Detection: A Deep Learning Approach with Electronic Medical Record System Integration and Diverse Image Data,” Adv. Technol., vol. 5, no. 1, pp. 52–66, 2025, doi: 10.31357/ait.v5i01.8414.
[34] K. V. Paluri and A. Gupta, “ETLoViT: An acne diagnosis approach using vision transformers and model ensembling,” Mach. Learn.: Sci. Technol., vol. 6, no. 3, Art. no. 035028, 2024, doi: 10.1088/2631-8695/ad7ad9.
[35] B. Shetty, N. Shetty, S. Suvarna, and P. R. Kolte, “Dermavision: Transforming Skincare, One Recommendation at a Time,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 13, no. 5, pp. 2890–2897, 2025, doi: 10.22214/ijraset.2025.70322.
[36] D. Ali, S. Azzaben, and R. Bajaman, “Optimized Acne Classification Using Transfer Learning with DenseNet121 and ResNet152,” in Proc. 2025 16th Student Res. Conf. Appl. Comput. (SRC), 2025, pp. 1–5, doi: 10.1109/SRC65875.2025.11263689.
[2] N. U. R. Sifatullah, “Jerawat (Acne vulgaris): Review Penyakit Infeksi Pada Kulit,” Pros. Semin. Nas. Biol., vol. 7, no. 1, pp. 19–23, 2021, doi: 10.24252/psb.v7i1.22212.
[3] A. M. Layton, V. Bettoli, V. Delore, E. Puentes, and J. K. L. Tan, “The Burden of Acne Vulgaris on Health-Related Quality of Life and Psychosocial Well-Being Domains: A Systematic Review,” Am. J. Clin. Dermatol., vol. 27, no. 1, pp. 17–47, 2025, doi: 10.1007/s40257-025-00983-3.
[4] R. F. Autrilia and R. H. Ninin, “Eksplorasi Dampak Psikologis pada Remaja yang Memiliki Masalah Penampilan dengan Jerawat,” J. Psikol. Udayana, vol. 9, no. 2, pp. 194–205, 2022, doi: 10.24843/jpu.2022.v09.i02.p09.
[5] V. D. Br Sebayang and I. G. N. L. W. Kusuma, “Klasifikasi Jenis Jerawat Berdasarkan Citra Menggunakan Convolutional Neural Network dengan Arsitektur MobileNetV2,” J. FASILKOM, vol. 14, no. 3, pp. 766–774, 2024, doi: 10.37859/jf.v14i3.8202.
[6] D. O. Traini, G. Palmisano, C. Guerriero, and K. Peris, “Artificial Intelligence in the Assessment and Grading of Acne Vulgaris: A Systematic Review,” J. Pers. Med., vol. 15, no. 6, Art. no. 238, 2025, doi: 10.3390/jpm15060238.
[7] D. Hussain and Y. H. Gu, “Exploring the Impact of Noise and Image Quality on Deep Learning Performance in DXA Images,” Diagnostics, vol. 14, no. 13, Art. no. 1328, 2024, doi: 10.3390/diagnostics14131328.
[8] Z. Younis, M. S. M. Rahim, and F. Bin Mohamed, “Enhancement of Unevenly Illuminated Images: An Experiment Based Review,” PROtek J. Ilm. Tek. Elektro, vol. 12, no. 2, pp. 112–120, 2025, doi: 10.33387/protk.v12i2.8533.
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[10] J. A. Rodríguez-Rodríguez, E. López-Rubio, J. A. Ángel-Ruiz, and M. A. Molina-Cabello, “The Impact of Noise and Brightness on Object Detection Methods,” Sensors, vol. 24, no. 3, Art. no. 821, 2024, doi: 10.3390/s24030821.
[11] M. Resa, A. Yudianto, P. Sukmasetya, R. A. Hasani, and D. Sasongko, “Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1367–1375, 2022, doi: 10.47065/bits.v4i3.2575.
[12] N. S. Suriani, S. S. A. Tarmizi, M. N. H. Mohd, and S. M. Shah, “Acne Severity Classification on Mobile Devices using Lightweight Deep Learning Approach,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 6, pp. 680–687, 2024, doi: 10.14569/IJACSA.2024.0150668.
[13] K. Watanabe, K. Iinuma, C. Nakashima, H. Yamamoto, N. Oiso, and A. Otsuka, “Deep Learning-Based Acne Severity Classification Using Standardized Facial Images of Japanese Patients,” Cureus, vol. 17, no. 9, Art. no. e91944, 2025, doi: 10.7759/cureus.91944.
[14] F. Ramadhani, S. Rahardiantoro, and M. Masjkur, “Acne Severity Classification Study Using Convolutional Neural Network Algorithm with MobileNetV2 Architecture,” Indones. J. Stat. Its Appl., vol. 8, no. 2, pp. 112–128, 2024, doi: 10.29244/ijsa.v8i2p112-128.
[15] S. J. Kuryanti et al., “Peningkatan Kualitas Citra Bawah Air Berbasis Algoritma Fusion Dengan Keseimbangan Warna, Optimalisasi Kontras,” INTI Nusa Mandiri, vol. 16, no. 1, pp. 1–8, 2021, doi: 10.33480/inti.v16i1.2286.
[16] J. Maus et al., “Deep Learning Based Bilateral Filtering for Edge-Preserving Denoising of Respiratory-Gated PET,” EJNMMI Phys., vol. 11, Art. no. 58, 2024, doi: 10.1186/s40658-024-00661-z.
[17] G. A. Alhamzawi, A. S. Alfoudi, and A. H. Alsaeedi, “Fusion Deep Learning with Adaptive Gamma Correction (DLAGC) to Enhance Images in Low Light,” J. Inf. Syst. Eng. Manag., vol. 10, no. 36s, pp. 882–893, 2025, doi: 10.52783/jisem.v10i36s.6610.
[18] M. A. Ghofur and A. B. Ulum, “Analisis Perbandingan Kinerja Model ResNet50 Dan EfficientNet-B0 Dalam Klasifikasi Kerusakan Jalan,” J. Media Inform., vol. 6, no. 5, pp. 2504–2511, 2025, doi: 10.55338/jumin.v6i5.6843.
[19] I. de Zarza, J. de Curto, and C. T. Calafate, “Detection of glaucoma using three-stage training with EfficientNet,” Intell. Syst. Appl., vol. 16, Art. no. 200140, 2022, doi: 10.1016/j.iswa.2022.200140.
[20] M. Y. Shams et al., “Skin Disease Classification: A Comparison of ResNet50, MobileNet, and Efficient-B0,” J. Curr. Multidiscip. Res., vol. 1, no. 1, pp. 1–7, 2025, doi: 10.21608/jcmr.2025.327880.1002.
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[22] M. I. Anshori, R. A. Yaqin, M. Ali, Z. Sidiq, and W. P. Agung, “Klasifikasi Jenis Jerawat Secara Otomatis Dengan Convolutional Neural Network Menggunakan Arsitektur ResNet-50,” J. Manaj. Inform., vol. 15, no. 1, pp. 73–84, 2025, doi: 10.34010/jamika.v15i1.13712.
[23] S. N. Hasan, “Accurate Deep Learning Algorithms for Skin Lesion Classification,” Ing. Syst. Inf., vol. 29, no. 4, pp. 1529–1539, 2024, doi: 10.18280/isi.290426.
[24] A. B. Abdusalomov, M. Mukhiddinov, and T. K. Whangbo, “Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging,” Cancers, vol. 15, no. 16, Art. no. 4172, 2023, doi: 10.3390/cancers15164172.
[25] R. Lathiya Teksun, “Acne Grading Classification Dataset,” Kaggle, 2020. [Online]. Available: https://www.kaggle.com/datasets/rutviklathiyateksun/acne-grading-classificationdataset. [Accessed: Nov. 2025].
[26] A. Apicella, F. Isgrò, and R. Prevete, “Don’t push the button! Exploring data leakage risks in machine learning and transfer learning,” Artif. Intell. Rev., vol. 58, Art. no. 339, 2025, doi: 10.1007/s10462-025-11326-3.
[27] M. I. Burhanuddin, A. Syaifullah, S. A. P. Jaya, and M. G. Somoal, “Analisis Komparatif Model MobilenetV1 Dan EfficientnetB0 Dalam Klasifikasi Citra Empat Musim Menggunakan Transfer Learning,” J. Tek. Inform., vol. 5, no. 2, pp. 508–521, 2025, doi: 10.58794/jekin.v5i2.1378.
[28] A. L. Rukuna et al., “Enhancing Monkeypox Detection with Efficientnet-B5 And Image Augmentation Fusion Technique,” Int. J. Sci. Res. Sci. Technol., vol. 11, no. 6, pp. 646–661, 2024, doi: 10.32628/IJSRST241161119.
[29] K. Kanchana, S. Kavitha, K. J. Anoop, and B. Chinthamani, “Enhancing Skin Cancer Classification Using Efficient Net B0-B7 through Convolutional Neural Networks and Transfer Learning with Patient-Specific Data,” Asian Pac. J. Cancer Prev., vol. 25, no. 5, pp. 1795–1802, 2024, doi: 10.31557/APJCP.2024.25.5.1795.
[30] R. I. Sumon, H. Ali, S. Akter, S. Muhammad, and I. Uddin, “A Deep Learning-Based Approach for Precise Emotion Recognition in Domestic Animals Using EfficientNetB5 Architecture,” Engineering, vol. 6, no. 1, Art. no. 9, 2025, doi: 10.3390/eng6010009.
[31] A. Nur, A. Thohari, L. Triyono, I. Hestiningsih, B. Suyanto, and A. Yobioktobera, “Performance Evaluation of Pre-Trained Convolutional Neural Network Model for Skin Disease Classification,” JUITA J. Inform., vol. 10, no. 1, pp. 9–18, 2022, doi: 10.30595/juita.v10i1.12041.
[32] R. S. Pradini, W. T. Kusuma, and A. S. Budi, “Convolutional neural network model for fingerprint-based gender classification using original and degraded images,” Int. J. Adv. Appl. Sci., vol. 14, no. 4, pp. 1350–1358, 2025, doi: 10.11591/ijaas.v14.i4.pp1350-1358.
[33] A. P. Singhapathiranage, D. D. K. Kelaart, A. A. M. Thathsarani, C. D. L. Liyanage, and B. N. S. Lankasena, “Optimizing Acne Severity Detection: A Deep Learning Approach with Electronic Medical Record System Integration and Diverse Image Data,” Adv. Technol., vol. 5, no. 1, pp. 52–66, 2025, doi: 10.31357/ait.v5i01.8414.
[34] K. V. Paluri and A. Gupta, “ETLoViT: An acne diagnosis approach using vision transformers and model ensembling,” Mach. Learn.: Sci. Technol., vol. 6, no. 3, Art. no. 035028, 2024, doi: 10.1088/2631-8695/ad7ad9.
[35] B. Shetty, N. Shetty, S. Suvarna, and P. R. Kolte, “Dermavision: Transforming Skincare, One Recommendation at a Time,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 13, no. 5, pp. 2890–2897, 2025, doi: 10.22214/ijraset.2025.70322.
[36] D. Ali, S. Azzaben, and R. Bajaman, “Optimized Acne Classification Using Transfer Learning with DenseNet121 and ResNet152,” in Proc. 2025 16th Student Res. Conf. Appl. Comput. (SRC), 2025, pp. 1–5, doi: 10.1109/SRC65875.2025.11263689.
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2026-06-30
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Rachmasari, D. K., Pradini, R. S., & Khudori, A. N. (2026). Analysis of Image Preprocessing on EfficientNet-B5 Performance in Acne Severity Classification . Jurnal Sistem Komputer Dan Informatika (JSON), 7(4), 1583–1592. https://doi.org/10.30865/json.v7i4.9818
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