Impact of Optimizer Selection on MobileNetV1 Performance for Skin Disease Detection Using Digital Images

Fathan Habie, Khairul and Murinto, Murinto and Sunardi, Sunardi (2025) Impact of Optimizer Selection on MobileNetV1 Performance for Skin Disease Detection Using Digital Images. [Artikel Dosen]

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Abstract

Automatic detection of skin diseases using digital images is a growing field in the application of deep learning in the
medical world, especially to help the early diagnosis process. One of the most widely used models is MobileNetV1
because it is lightweight and efficient in image processing. However, the performance of the model is greatly affected
by the training configuration, including the type of optimizer used. This study aims to compare the effectiveness of six types of optimizers, namely SGD, RMSprop, Adam, Adadelta, Adagrad, Adamax, and Nadam in training
MobileNetV1 models for human skin disease image classification. The model was trained on annotated skin image dataset with predetermined training parameters: batch size 32, learning rate of 0.0001, and 10 epochs. Performance
evaluation was performed using accuracy metrics. The results obtained demonstrate that RMSprop performs best,
with 99.10% accuracy, 99.14% precision, 99.10% recall, and a 99.10% F1-score. Adadelta showed the lowest
performance consistently, with only 22.22% accuracy, 20.34% precision, 22.22% recall, and 18.42% F1-score. This
finding confirms that the type of optimizer affects the effectiveness of model training, especially in medical image
classification tasks. This research provides empirical insights that are useful in selecting the optimal optimizer for MobileNetV1 model implementation in the healthcare domain

Item Type:	Artikel Dosen
Subjects:	T Technology > T Technology (General)
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Informatics Engineering (S1-Teknik Informatika)
Depositing User:	murinto murinto
Date Deposited:	26 Aug 2025 03:09
Last Modified:	26 Aug 2025 03:09
URI:	http://eprints.uad.ac.id/id/eprint/86458

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