  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.
