Anan Wibowo, Rahmat Widia Sembiring, Solikhun Solikhun

Deep Learning Based MobileNet Optimization For High Accuracy Classification Of Toddler Stunting

Introduction

Deep learning based mobilenet optimization for high accuracy classification of toddler stunting . Optimized MobileNet deep learning achieves 100% accuracy classifying toddler stunting using whole-body images. Novel and efficient for clinical screening.

Abstract

This study aims to develop and optimize a MobileNet-based deep learning model for toddler stunting classification using whole-body images. A progressive optimization strategy was applied through three scenarios: (1) a baseline MobileNet feature-extraction model, (2) an optimized fine-tuned model, and (3) a final model enhanced with an adaptive ReduceLROnPlateau scheduler. Using a private dataset of 571 images, the proposed model achieved significant improvements—from 97.47% accuracy in the baseline model to a perfect 100% accuracy, precision, recall, and F1-score in the final scenario. These results highlight the novelty of this study, namely the use of whole-body images combined with progressive MobileNet optimization, which substantially outperforms prior studies relying solely on facial image analysis. The proposed approach demonstrates strong potential as a highly accurate and efficient computational tool for clinical stunting screening.

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