Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification

Accurately identifying facial skin types is essential for recommending the right skincare treatments and products. Misidentifying skin types can lead to negative consequences, such as irritation or worsening of skin conditions. This study investigated methods for classifying facial skin types into f...

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Main Authors: Herimanto, Arie Satia Dharma, Junita Amalia, David Largo, Christin Adelia Pratiwi Sihite
Format: Article
Language:English
Published: Ikatan Ahli Informatika Indonesia 2024-12-01
Series:Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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Online Access:https://jurnal.iaii.or.id/index.php/RESTI/article/view/5982
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author Herimanto
Arie Satia Dharma
Junita Amalia
David Largo
Christin Adelia Pratiwi Sihite
author_facet Herimanto
Arie Satia Dharma
Junita Amalia
David Largo
Christin Adelia Pratiwi Sihite
author_sort Herimanto
collection DOAJ
description Accurately identifying facial skin types is essential for recommending the right skincare treatments and products. Misidentifying skin types can lead to negative consequences, such as irritation or worsening of skin conditions. This study investigated methods for classifying facial skin types into five categories: oily, acne-prone, dry, normal, and combination. A dataset of 1725 augmented facial images was used. Data augmentation techniques likely increased the dataset's diversity, which helps improve the model's generalization ability. The data underwent preprocessing, including rescaling, before being applied to two deep learning models, CNN and MobileNetV3. The models were evaluated based on accuracy and execution time to determine the most effective approach for classifying facial skin types. The CNN model achieved an accuracy of 64%, demonstrating its potential for image classification tasks. However, the MobileNetV3 model significantly outperformed CNN with an accuracy of 84%. This superior performance is attributed to MobileNetV3's advanced architecture, which is optimized for efficient feature extraction, and particularly relevant for capturing the subtle variations in facial skin types. Therefore, MobileNetV3 emerged as the more effective method for classifying facial skin types with higher accuracy.
format Article
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institution Kabale University
issn 2580-0760
language English
publishDate 2024-12-01
publisher Ikatan Ahli Informatika Indonesia
record_format Article
series Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
spelling doaj-art-f9a6c4ea7d4944b69e77e290985917052025-01-13T03:30:33ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-12-018670170910.29207/resti.v8i6.59825982Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder ClassificationHerimanto0Arie Satia Dharma1Junita Amalia2David Largo3Christin Adelia Pratiwi Sihite4Institut Teknologi DelInstitut Teknologi DelInstitut Teknologi DelInstitut Teknologi DelInstitut Teknologi DelAccurately identifying facial skin types is essential for recommending the right skincare treatments and products. Misidentifying skin types can lead to negative consequences, such as irritation or worsening of skin conditions. This study investigated methods for classifying facial skin types into five categories: oily, acne-prone, dry, normal, and combination. A dataset of 1725 augmented facial images was used. Data augmentation techniques likely increased the dataset's diversity, which helps improve the model's generalization ability. The data underwent preprocessing, including rescaling, before being applied to two deep learning models, CNN and MobileNetV3. The models were evaluated based on accuracy and execution time to determine the most effective approach for classifying facial skin types. The CNN model achieved an accuracy of 64%, demonstrating its potential for image classification tasks. However, the MobileNetV3 model significantly outperformed CNN with an accuracy of 84%. This superior performance is attributed to MobileNetV3's advanced architecture, which is optimized for efficient feature extraction, and particularly relevant for capturing the subtle variations in facial skin types. Therefore, MobileNetV3 emerged as the more effective method for classifying facial skin types with higher accuracy.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5982cnndeep learningmobilenetv3skin classification
spellingShingle Herimanto
Arie Satia Dharma
Junita Amalia
David Largo
Christin Adelia Pratiwi Sihite
Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
cnn
deep learning
mobilenetv3
skin classification
title Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification
title_full Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification
title_fullStr Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification
title_full_unstemmed Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification
title_short Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification
title_sort performance analysis of mobilenetv3 based convolutional neural network for facial skin disorder classification
topic cnn
deep learning
mobilenetv3
skin classification
url https://jurnal.iaii.or.id/index.php/RESTI/article/view/5982
work_keys_str_mv AT herimanto performanceanalysisofmobilenetv3basedconvolutionalneuralnetworkforfacialskindisorderclassification
AT ariesatiadharma performanceanalysisofmobilenetv3basedconvolutionalneuralnetworkforfacialskindisorderclassification
AT junitaamalia performanceanalysisofmobilenetv3basedconvolutionalneuralnetworkforfacialskindisorderclassification
AT davidlargo performanceanalysisofmobilenetv3basedconvolutionalneuralnetworkforfacialskindisorderclassification
AT christinadeliapratiwisihite performanceanalysisofmobilenetv3basedconvolutionalneuralnetworkforfacialskindisorderclassification