International Journal of Advanced Technology and Engineering Exploration ISSN (Print): 2394-5443    ISSN (Online): 2394-7454 Volume-12 Issue-122 January-2025
  1. 4774
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  2. 2.8
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Convolutional neural network architectural models for multiclass classification of aesthetic facial skin disorders

Rismayani1, 2, Amil Ahmad Ilham3, Andani Achmad1 and Muhammad Rifqy Yudhiestra Rachman3

Department of Electrical Engineering,Faculty of Engineering, Universitas Hasanuddin, Gowa,Indonesia1
Department of Electrical Engineering,Dipa Makassar University, Makassar,Indonesia2
Department of Informatics,Faculty of Engineering, Universitas Hasanuddin, Gowa,Indonesia3
Corresponding Author : Amil Ahmad Ilham

Recieved : 21-June-2024; Revised : 17-January-2025; Accepted : 19-January-2025

Abstract

Aesthetic facial skin disorders present a significant challenge in dermatology, often requiring accurate diagnosis for effective treatment. This study investigates the effectiveness of convolutional neural network (CNN) architectural models for multiclass classification of facial skin disorders, including oily skin, hyperpigmentation, acne, redness, blackheads, and normal skin types. The research aims to compare the performance of various CNN architectures, including EfficientNetB0, HRNet, DenseNet201, and ResNet50, in classifying facial skin image datasets into multiple categories of aesthetic skin disorders. Additionally, this study examines the impact of feature combination methods on classification accuracy by enhancing attribute representation. Specifically, it explores the integration of color moment (CM) for color features and Laplacian of Gaussian (LoG) for shape features with CNN architectural models. Performance evaluation and comparison are conducted between CNN architectures with and without feature combinations. The results demonstrate that the selected CNN architectures effectively classify various aesthetic facial skin disorders, achieving an accuracy rate of 0.95 using the ResNet50 architecture. Moreover, the findings highlight that feature combination techniques, particularly the integration of CNN architectures with CM and LoG, significantly enhance classification accuracy. This study emphasizes the potential of CNN architectural models to improve early diagnostic capabilities in dermatology. The integration of artificial intelligence (AI)-based technology in the classification of aesthetic facial skin disorders offers a promising avenue for more accurate and effective diagnosis and treatment, ultimately improving patient outcomes.

Keywords

Aesthetic facial skin disorders, Convolutional neural network, Multiclass classification, Feature combination methods, Artificial intelligence.

Cite this article

Rismayani, Ilham AA, Achmad A, Rachman MRY. Convolutional neural network architectural models for multiclass classification of aesthetic facial skin disorders. International Journal of Advanced Technology and Engineering Exploration. 2025;12(122):112-131. DOI : 10.19101/IJATEE.2024.111101077

References
[1]
Yang TT, Lan CC. Impacts of skin disorders associated with facial discoloration on quality of life: novel insights explaining discordance between life quality scores and willingness to pay. Journal of Cosmetic Dermatology. 2022; 21(7):3053-8.
[Crossref] [Google Scholar]
[2]
Shekhar N, Eeshaan R, Tripathy DM, Siddharth M, Bhavni O. Pigmented purpuric dermatoses: a review. Pigment International. 2024; 11(1):1-11.
[Google Scholar]
[3]
Nandraj JO, Gajanan KA, Karande KM. A concise review on contemporary and novel treatments addressing the prevention and control of hyperpigmentation. Asian Journal of Pharmaceutical Research and Development. 2024; 12(2):19-27.
[Crossref] [Google Scholar]
[4]
Darwish E, Abdelgawad W, Makhlouf M, Abdalla H, Nassar YM, El-tohamy MH, et al. A mobile-based deep learning system for skin disease diagnosis. In intelligent methods, systems, and applications 2024 (pp. 39-44). IEEE.
[Crossref] [Google Scholar]
[5]
Raman R, Kumar V, Saini D, Rabadiya D, Patre S, Meenakshi R. Machine learning-driven approaches for dermatological disease diagnosis. In international conference on data science and network security 2024 (pp. 1-5). IEEE.
[Crossref] [Google Scholar]
[6]
Malik SG, Jamil SS, Aziz A, Ullah S, Ullah I, Abohashrh M. High-precision skin disease diagnosis through deep learning on dermoscopic images. Bioengineering. 2024; 11(9):1-24.
[Crossref] [Google Scholar]
[7]
Pugazhenthi V, Naik SK, Joshi AD, Manerkar SS, Nagvekar VU, Naik KP, et al. Skin disease detection and classification. International Journal of Advanced Engineering Research and Science. 2019; 6(5):396-400.
[Crossref] [Google Scholar]
[8]
Wu X, Feng Y, Xu H, Lin Z, Chen T, Li S, et al. CTransCNN: combining transformer and CNN in multilabel medical image classification. Knowledge-Based Systems. 2023; 281:1-15.
[Crossref] [Google Scholar]
[9]
Azad R, Kazerouni A, Heidari M, Aghdam EK, Molaei A, Jia Y, et al. Advances in medical image analysis with vision transformers: a comprehensive review. Medical Image Analysis. 2024; 91:103000.
[Crossref] [Google Scholar]
[10]
Ilham AA, Achmad A, Rachman MR. Facial skin disorder prediction based on non-visual information using ANN model. In 7th international conference on informatics and computational sciences 2024 (pp. 450-5). IEEE.
[Crossref] [Google Scholar]
[11]
Wang Y, Li D, Li L, Sun R, Wang S. A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model. Heliyon. 2024; 10(15):1-11.
[Crossref] [Google Scholar]
[12]
Wu ZH, Zhao S, Peng Y, He X, Zhao X, Huang K, et al. Studies on different CNN algorithms for face skin disease classification based on clinical images. IEEE Access. 2019; 7:66505-11.
[Crossref] [Google Scholar]
[13]
Pawshe V, Bhagwat A, Yelai S, Irkar S, Kornule P. Face skin disease classification based on images. Journal of Emerging Technologies and Innovative Research. 2020; 7(6): 384-7.
[14]
Srinivasu PN, Sivasai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ. Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors. 2021; 21(8):1-27.
[Crossref] [Google Scholar]
[15]
Antoniadi AM, Du Y, Guendouz Y, Wei L, Mazo C, Becker BA, et al. Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Applied Sciences. 2021; 11(11):1-23.
[Crossref] [Google Scholar]
[16]
Dai Y. Building CNN-based models for image aesthetic score prediction using an ensemble. Journal of Imaging. 2023; 9(2):1-14.
[Crossref] [Google Scholar]
[17]
Li Z, Koban KC, Schenck TL, Giunta RE, Li Q, Sun Y. Artificial intelligence in dermatology image analysis: current developments and future trends. Journal of Clinical Medicine. 2022; 11(22):1-14.
[Crossref] [Google Scholar]
[18]
Sazzadul IPM, Mahjabin FS, Bulbul AM, Zihadur RM, Kabir HAB, Shamim KM. Deep learning-based skin disease detection using convolutional neural networks (CNN). In the fourth industrial revolution and beyond: select proceedings of IC4IR+ 2023 (pp. 551-64). Singapore: Springer Nature Singapore.
[Crossref] [Google Scholar]
[19]
Dhar P, Guha S. Skin lesion detection using fuzzy approach and classification with CNN. International Journal of Engineering and Manufacturing. 2021; 11(1):11-8.
[Crossref] [Google Scholar]
[20]
Thwe PM, Yu MT. Analysis on skin colour model using adaptive threshold values for hand segmentation. International Journal of Image, Graphics and Signal Processing 2019; 11(9):25-33.
[Crossref] [Google Scholar]
[21]
Chickaramanna SG, Thippeswamy VS. Identification and classification of prakriti of human using facial features. IAES International Journal of Artificial Intelligence. 2024; 13(2):2093-101.
[Crossref]
[22]
Dahdouh Y, Boudhiranouar A, Ahmed M. Embedded artificial intelligence system using deep learning and raspberrypi for the detection and classification of melanoma. IAES International Journal of Artificial Intelligence. 2024; 13(1): 1104-11.
[Crossref]
[23]
Gadag S, Palraj P. Hybrid channel and spatial attention-UNet for skin lesion segmentation. IAES International Journal of Artificial Intelligence (IJ-AI). 2024; 13(1):1077-89.
[Crossref] [Google Scholar]
[24]
Khomsi Z, El FM, Bellarbi L. CNN-based approach for non-invasive estimation of breast tumor size and location using thermographic images. International Journal of Online & Biomedical Engineering. 2024; 20(4):160-75.
[Crossref] [Google Scholar]
[25]
Makhir A, El YMH, Alaoui LB. Comprehensive cardiac ischemia classification using hybrid CNN-based models. International Journal of Online & Biomedical Engineering. 2024; 20(3):154-65.
[Crossref] [Google Scholar]
[26]
Maquen-niño GL, Nuñez-fernandez JG, Taquila-falderon FY, Adrianzén-olano I, De-la-cruz-VdV P, Carrión-barco G. Classification model using transfer learning for the detection of pneumonia in chest X-ray images. International Journal of Online & Biomedical Engineering. 2024; 20(5):150-61.
[Crossref] [Google Scholar]
[27]
Benradi H, Bouganssa I, Chater A, Lasfar A. Discriminative approach lung diseases and COVID-19 from chest X-Ray images using convolutional neural networks: a promising approach for accurate diagnosis. International Journal of Online & Biomedical Engineering. 2023; 19(14):131-41.
[Crossref] [Google Scholar]
[28]
Allugunti VR. A machine learning model for skin disease classification using convolution neural network. International Journal of Computing, Programming and Database Management. 2022; 3(1):141-7.
[Google Scholar]
[29]
Bonechi S, Bianchini M, Bongini P, Ciano G, Giacomini G, Rosai R, et al. Fusion of visual and anamnestic data for the classification of skin lesions with deep learning. In new trends in image analysis and processing–ICIAP Trento, Italy, 2019 (pp. 211-9). Springer International Publishing.
[Crossref] [Google Scholar]
[30]
Pacheco AG, Krohling RA. The impact of patient clinical information on automated skin cancer detection. Computers in Biology and Medicine. 2020; 116:103545.
[Crossref] [Google Scholar]
[31]
Chin YP, Hou ZY, Lee MY, Chu HM, Wang HH, Lin YT, et al. A patient‐oriented, general‐practitioner‐level, deep‐learning‐based cutaneous pigmented lesion risk classifier on a smartphone. British Journal of Dermatology. 2020; 182(6):1498-500.
[Crossref] [Google Scholar]
[32]
Sriwong K, Bunrit S, Kerdprasop K, Kerdprasop N. Dermatological classification using deep learning of skin image and patient background knowledge. International Journal of Machine Learning and Computing. 2019; 9(6):862-7.
[Crossref] [Google Scholar]
[33]
Nath S, Das GS, Saha S. Deep learning-based common skin disease image classification. Journal of Intelligent & Fuzzy Systems. 2023; 44(5):7483-99.
[Crossref] [Google Scholar]