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All Journal Jurnal INFOTEL
Nurlida Basir
Universiti Sains Islam Malaysia, Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Autism Face Detection System using Single Shot Detector and ResNet50 Melinda Melinda; Muhammad Fauzan Alfariz; Yunidar Yunidar; Agung Hilm Ghimri; Maulisa Oktiana; Rizka Miftahujjannah; Nurlida Basir; Donata D. Acula
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1331

Abstract

The facial features of children can provide important visual cues for the early detection of autism spectrum disorder (ASD). This research focuses on developing an image-based detection system to identify children with ASD. The main problem addressed is the lack of practical methods to assist healthcare professionals in the early identification of ASD through facial visual characteristics. This study aims to design a prototype facial image acquisition and detection system for children with ASD using Raspberry Pi and a deep learning-based single shot detector (SSD) algorithm. In this method, the face detection model uses a modified ResNet50 architecture, which can be used for advanced analysis for classification between autistic and normal children, achieving 95% recognition accuracy on a dataset consisting of facial images of children with and without ASD. The system is able to recognize the visual characteristics of the faces of children with ASD and consistently distinguish them from those of normal children. Real-time testing shows a detection accuracy ranging from 86% to 90%, with an average accuracy of 90%, despite fluctuations caused by variations in movement and viewing angle. These results show that the developed system offers high accuracy and has the potential to function as a reliable diagnostic tool for the early detection of ASD, which ultimately facilitates timely intervention by healthcare professionals to support the optimal development of children with ASD.
Robust Facial Classification of Down Syndrome using Lightweight CNNs Yunidar Wahab; Muhammad Dika Rafi Kasha; Melinda Melinda; Nurlida Basir; Siti Rusdiana
JURNAL INFOTEL Vol 18 No 1 (2026): February
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v18i1.1525

Abstract

Down Syndrome (DS) is a genetic disorder caused by trisomy 21 and is characterized by distinctive facial features that can support early screening. However, access to conventional diagnostic tools remains limited, particularly in resource-constrained regions. This study presents a comparative evaluation of two lightweight convolutional neural network (CNN) architectures, EfficientNet-B1 and MobileNetV3-Large, for facial image-based DS classification. A curated dataset of 3,030 facial images underwent quality control and image enhancement processes applied exclusively to the training data, resulting in 2,620 images. The dataset was split into training, validation, and test sets at a 70:20:10 ratio. Both models were fine-tuned using ImageNet-pretrained weights and evaluated based on accuracy, precision, recall, and F1-score. Performance robustness and statistical significance between models were assessed using five-fold cross-validation and one-way ANOVA. The experimental results demonstrate that both architectures achieved high classification performance; however, EfficientNet-B1 exhibited superior stability, more balanced class predictions, and lower fold-to-fold variability. Furthermore, Grad-CAM visualization confirmed that both models focused on clinically relevant facial regions, with EfficientNet-B1 showing more consistent and interpretable attention patterns. These findings suggest that EfficientNet-B1 is a robust and interpretable model for facial-based DS screening, offering significant potential for deployment in resource-limited healthcare settings.
Co-Authors Agung Hilm Ghimri Donata D. Acula Maulisa Oktiana Melinda Melinda Muhammad Dika Rafi Kasha Muhammad Fauzan Alfariz Rizka Miftahujjannah Siti Rusdiana Yunidar Wahab Yunidar Yunidar