Involution Fused ConvNet for Classifying Eye-Tracking Patterns of
  Children with Autism Spectrum Disorder
Md. Farhadul Islam Meem Arafat Manab Joyanta Jyoti Mondal Sarah Zabeen Fardin Bin Rahman Md. Zahidul Hasan Farig Sadeque Jannatun Noor
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.