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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes
Melinda Melinda
Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science

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Classification of Autism Spectrum Disorder (ASD) in Children Using the VGG19 CNN Model Based on Facial Landmarks of the Eye and Forehead Areas yunidar; Arya Suyanda; Melinda Melinda; Lailatul Qadri Zakaria; Siti Rusdiana
Jurnal Teknokes Vol. 19 No. 2 (2026): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v19i2.158

Abstract

Early detection of Autism Spectrum Disorder (ASD) is a crucial challenge in child development interventions because conventional screening methods are often subjective and prone to assessor bias. This study proposes an objective solution in the form of a deep learning approach for automatic ASD classification using facial landmark representations that focus exclusively on the eye and forehead areas. The selection of these areas is based on the eye avoidance hypothesis, which states that these regions contain very rich diagnostic information and behavioral biomarkers related to the ASD phenotype. The pre-processing stage involves isolating the eye and forehead areas using Dlib 68-landmark detection to eliminate background visual noise, followed by detailed topological visualization using MediaPipe Face Mesh with 478 landmark points as the model input. The Convolutional Neural Network (CNN) architecture used is the VGG19 model modified with transfer learning techniques and the addition of Dropout layers to improve efficiency and prevent overfitting. The model was trained on a primary dataset of 1,238 images collected under controlled conditions from children in Banda Aceh. The test results showed very promising performance with an overall accuracy of 94.35%. Specifically, the model achieved a recall (sensitivity) of 95.24%, a precision of 93.75%, and an AUC score of 0.9831. This high sensitivity is crucial in a medical context to minimize the risk of misdetection of positive cases. These results demonstrate that landmark visualization in the eye and forehead areas with the VGG19 model is a highly effective, accurate, and practical method for serving as an economical early screening tool for ASD.
CNN-Based Facial Image Analysis for Pediatric Down Syndrome Classification Yunidar Yunidar; Inda Mariana Harahap; Melinda Melinda; Rosmawinda Rosmawinda; Nurlida Basir; Aufa Rafiki; Imam Fathur Rahman
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1523

Abstract

Down syndrome (trisomy 21) is a genetic disorder caused by an extra copy of chromosome 21, resulting in distinctive developmental facial characteristics and intellectual delays. Early detection is crucial to enable timely medical intervention. However, conventional diagnostic procedures still rely on clinical observation and genetic testing, which can be invasive and expensive. This study proposes a facial image–based classification system for detecting Down syndrome using a Convolutional Neural Network (CNN) approach. Seven CNN architectures were evaluated, namely EfficientNetB0, MobileNetV2, ResNet34, ShuffleNetV2, AlexNet, VGG19, and InceptionV3, under two training scenarios: with and without early stopping. The dataset consisted of 1,000 facial images of children with and without Down syndrome, split into training, validation, and test sets at 60:20:20. Face detection was performed using the Haar Cascade Classifier, followed by data augmentation techniques including rotation, zoom, translation, horizontal flipping, and Gaussian noise to improve model generalization and reduce overfitting. Experimental results show that the VGG19 architecture achieved the best performance, with an accuracy of 94.5%, precision of 91.59%, recall of 98%, and an F1-score of 94.69%. A one-way ANOVA test yielded an F-value of 0.003 and a p-value of 0.955 (> 0.05), indicating no statistically significant difference between models trained with and without early stopping. Grad-CAM visualization highlighted key facial regions, namely the eyes, nose, and mouth, as the primary contributors to classification, while analysis using 68 facial landmark points revealed distinctive morphological patterns associated with Down syndrome. The integration of CNN models, Grad-CAM visualization, and facial landmark analysis demonstrates a promising, interpretable, and non-invasive approach to supporting early Down syndrome screening using facial images
Co-Authors Arya Suyanda Aufa Rafiki Imam Fathur Rahman Inda Mariana Harahap Lailatul Qadri Zakaria Nurlida Basir Rosmawinda Rosmawinda Siti Rusdiana yunidar Yunidar Yunidar