POWER OF ALIGNMENT: EXPLORING THE EFFECT OF FACE ALIGNMENT ON ASD DIAGNOSIS USING FACIAL IMAGES

Muhammad Mahbubur Rashid; Mohammad Shafiul Alam

doi:10.31436/iiumej.v25i1.2838

Authors

Muhammad Mahbubur Rashid International Islamic University Malaysia https://orcid.org/0000-0002-3520-0657
Mohammad Shafiul Alam International Islamic University Malaysia https://orcid.org/0009-0007-1653-8218

DOI:

https://doi.org/10.31436/iiumej.v25i1.2838

Keywords:

ASD, CNN, deep learning, Facial Expressions, Face Alignment

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder that impacts social communication and conduct. ASD lacks standard treatment protocols or medication, thus early identification and proper intervention are the most effective procedures to treat this disorder. Artificial intelligence could be a very effective tool to be used in ASD diagnosis as this is free from human bias. This research examines the effect of face alignment for the early diagnosis of Autism Spectrum Disorder (ASD) using facial images with the possibility that face alignment can improve the prediction accuracy of deep learning algorithms. This work uses the SOTA deep learning-based face alignment algorithm MTCNN to preprocess the raw data. In addition, the impacts of facial alignment on ASD diagnosis using facial images are investigated using state-of-the-art CNN backbones such as ResNet50, Xception, and MobileNet. ResNet50V2 achieves the maximum prediction accuracy of 93.97% and AUC of 96.33% with the alignment of training samples, which is a substantial improvement over previous research. This research paves the way for a data-centric approach that can be applied to medical datasets in order to improve the efficacy of deep neural network algorithms used to develop smart medical devices for the benefit of mankind.

ABSTRAK: Gangguan Spektrum Autisme (ASD) adalah gangguan perkembangan yang memberi kesan kepada komunikasi dan tingkah laku sosial. Kelemahan dalam rawatan ASD adalah ianya tidak mempunyai protokol rawatan standard atau ubat. Oleh itu pengenalan awal dan campur tangan betul merupakan prosedur paling berkesan bagi merawat gangguan ini. Kecerdasan buatan boleh menjadi alat berkesan bagi diagnosis ASD kerana bebas campur tangan manusia. Penyelidikan ini mengkaji kesan penjajaran muka bagi diagnosis awal ASD menggunakan imej muka dengan kebarangkalian penjajaran muka dapat meningkatkan ketepatan ramalan algoritma pembelajaran mendalam. Kajian ini menggunakan algoritma penjajaran muka MTCNN berasaskan pembelajaran mendalam SOTA bagi pra-proses data mentah. Selain itu, kesan penjajaran muka pada diagnosis ASD menggunakan imej muka disiasat menggunakan CNN terkini seperti ResNet50, Xception dan MobileNet. ResNet50V2 mencapai ketepatan ramalan maksimum sebanyak 93.97% dan AUC 96.33% dengan sampel penjajaran latihan, yang merupakan peningkatan ketara berbanding penyelidikan terdahulu. Kajian ini membuka jalan bagi pendekatan data berpusat yang boleh digunakan pada set data perubatan bagi meningkatkan keberkesanan algoritma rangkaian saraf mendalam dan membangunkan peranti perubatan pintar bermanfaat untuk manusia.

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Author Biography

Mohammad Shafiul Alam, International Islamic University Malaysia

Department of Mechatronics Engineering, International Islamic University Malaysia (IIUM),

Kuala Lumpur, Malaysia

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