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All Journal Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Knowledge Engineering and Data Science JIKO (Jurnal Informatika dan Komputer) Jurnal Teknologi Terapan METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Innovation Information Technology and Application (JINITA) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Adopsi Teknologi dan Sistem Informasi
Gubtha Mahendra Putra
Informatics Department, Engineering Faculty, Mulawarman University, Samarinda, 75123, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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Facial Image-Based Autism Detection Using ConvNeXt Tiny: A Lightweight Deep Learning Approach for Early Screening Putut Pamilih Widagdo; Muhammad Bambang Firdaus; Gubtha Mahendra Putra; Akhmad Irsyad
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2848

Abstract

This study proposes a deep learning model using the ConvNeXt Tiny architecture to detect autism spectrum disorder (ASD) from facial images, addressing the need for an early, efficient, and accessible diagnostic tool. The model integrates facial image preprocessing techniques like Contrast Limited Adaptive Histogram Equalization (CLAHE) and data augmentation, with facial segmentation performed by MTCNN. The ConvNeXt Tiny model is trained using transfer learning and evaluated through metrics such as accuracy, precision, recall, and F1-score, and compared with traditional CNN models like ResNet50 and EfficientNet-B0. The results demonstrate that the proposed model outperforms ResNet50 and EfficientNet in all evaluation metrics, achieving a classification accuracy of 84%. It also demonstrates a balanced performance across both classes (autistic and non-autistic), with high precision and recall for both, leading to a high F1-score. Furthermore, the model's computational efficiency makes it suitable for web and mobile applications, enabling scalable and real-time screening for ASD in children. The study's contributions include the development of a novel, lightweight ASD classification system, a comparative analysis of ConvNeXt with other CNN models, and the creation of a prototype for early ASD detection. This approach not only provides a promising alternative to conventional diagnostic methods but also sets the groundwork for further research and practical implementation in clinical settings.
Co-Authors Anam, M Khairul Anton Prafanto Arinda Mulawardani Kustiawan Awang Harsa Kridalaksana Budiman, Edy Dary Daris Abdurrahman Dedi Cahyadi Fahrul Agus Fandy Haviluddin Haviluddin Indah Fitri Astuti Irsyad, Akhmad Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Wisdan Pratama Putra Nariza Wanti Wulan Sari Novianti Puspitasari Pakpahan, Herman Santoso Puspitasari, Novianti Putut Pamilih Widagdo Rayner Alfred Reza Wardhana Rima Yustika Hasnida Rosmasari Rosmasari Setyadi, Hario Jati Solly Aryza Suryani Junita Patandianan Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Taruk, Medi Yumami, Eva