Impact of Optimizer Selection on MobileNetV1 Performance for Skin Disease Detection Using Digital Images - Habie, Khairul Fathan; Murinto, Murinto; Sunardi, Sunardi (Informatika, Universitas Jenderal Soedirman, 2025)
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.