Publication: Ethnicity-inclusive malnutrition detection with convolutional neural network in the philippines
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Date
2024-04
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Don Mariano Marcos Memorial State Univeristy - South La Union Campus
Abstract
In this study, the researchers developed a deep learning-based malnutrition
detection model for public elementary schools in La Union and Pangasinan. Their primary
objectives were to collect a suitable dataset, apply pre-processing techniques, train a
Convolutional Neural Network (CNN) using AlexNet architecture, and evaluate its
performance. They gathered 400 images from Bigbiga Elementary School and Sta. Rita
West Elementary School, pre-processed, and augmented the data to create 1,200 training
samples, 400 validation samples, and 80 test samples. Techniques like Horizontal Flip,
Brightness Adjustments, Noise Reduction, and Random Rotation were used to enhance
dataset quality. The CNN model trained on this data achieved a 66% test accuracy in
identifying malnutrition. This research provides a foundation for early malnutrition
detection, suggesting future improvements through additional pre-processing techniques,
dataset expansion, and integrating the model into applications for broader lise, potentially
improving public health outcomes
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Citation
Cerezo, A. G. S., Perez, J. P. R. B., Rocacorba, A. B., Soriben, J. M., & Valdez, A. V. (2024) Ethnicity-inclusive malnutrition detection with convolutional neural network in the philippines [Unpublished Undergraduate thesis]. Don Mariano Marcos Memorial State Univeristy - South La Union Campus, Agoo, La Union.