Involution Fused ConvNet for Classifying Eye-Tracking Patterns of Children with Autism Spectrum Disorder
| Autor: | Islam, Md. Farhadul, Manab, Meem Arafat, Mondal, Joyanta Jyoti, Zabeen, Sarah, Rahman, Fardin Bin, Hasan, Md. Zahidul, Sadeque, Farig, Noor, Jannatun |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Autism Spectrum Disorder (ASD) is a complicated neurological condition which is challenging to diagnose. Numerous studies demonstrate that children diagnosed with autism struggle with maintaining attention spans and have less focused vision. The eye-tracking technology has drawn special attention in the context of ASD since anomalies in gaze have long been acknowledged as a defining feature of autism in general. Deep Learning (DL) approaches coupled with eye-tracking sensors are exploiting additional capabilities to advance the diagnostic and its applications. By learning intricate nonlinear input-output relations, DL can accurately recognize the various gaze and eye-tracking patterns and adjust to the data. Convolutions alone are insufficient to capture the important spatial information in gaze patterns or eye tracking. The dynamic kernel-based process known as involutions can improve the efficiency of classifying gaze patterns or eye tracking data. In this paper, we utilise two different image-processing operations to see how these processes learn eye-tracking patterns. Since these patterns are primarily based on spatial information, we use involution with convolution making it a hybrid, which adds location-specific capability to a deep learning model. Our proposed model is implemented in a simple yet effective approach, which makes it easier for applying in real life. We investigate the reasons why our approach works well for classifying eye-tracking patterns. For comparative analysis, we experiment with two separate datasets as well as a combined version of both. The results show that IC with three involution layers outperforms the previous approaches. Comment: 17 pages, 13 figures, Submitted to Engineering Applications of Artificial Intelligence |
| Databáze: | arXiv |
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