CNNPerformanceImprovement forClassifying Stunted Facial ImagesUsingEarly Stopping Approach

CNNPerformanceImprovement forClassifying Stunted Facial ImagesUsingEarly Stopping Approach

CNN;early stopping; faces; Haar Cascade;stunting; stunted

Stunting, a condition characterisedby short stature, is a growth disorder caused by chronic malnutrition, which often begins in the womb. Children affected by stunting usually show different physical and cognitive characteristics compared to their peers. Research shows that these physical differences can also be observed in facial features. Because faces provide important information and are commonly studied in digital image processing, in this study, we will compare the facial image classification performance of stunted children versus normal children using various Convolutional Neural Network (CNN) architectures. The evaluated architectures include MobileNetV2, InceptionV3, VGG19, ResNet18, EfficientNetB0, and AlexNet. To improve the learning process, augmentation techniques with Haar cascade and Gaussian filters were applied so that the data set increased from 1,000 to 6,000 images. After adding the dataset, training is carried out with an early stop approach to minimiseoverfitting. The main aim of this research is to identify the CNN model that is most effective in differentiating facial images of stunted children from normal children. The results show that the EfficientNetB0 architecture outperforms other models, achieving 100% accuracy. Earlystopping has been shown to improve training efficiency and help prevent overfitting

Yunidar Yunidar1,2,Y Yusni1,3,NNasaruddin1,2Fitri Arnia*1

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6068/1010

Program Doktor Ilmu Teknik, Universitas Syiah Kuala, Banda Aceh, Indonesia

25-01-2025

FAJAR BAGUS W

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