Abstract:
The presented scientific work describes the results of the development and
evaluation of two deep learning algorithms: long short-term memory with a
convolutional neural network (LSTM+CNN) and long short-term memory
with an autoencoder (LSTM+AE), designed for the diagnosis of autism
spectrum disorders. The study focuses on the use of eye tracking technology
to collect data on participants' eye movements while interacting with
animated objects. These data were saved in NumPy array format (.npy) for
ease of later analysis. The algorithms were evaluated in terms of their
accuracy, generalization ability, and training time, which was confirmed by
experts. The main goal of the study is to improve the diagnosis of autism,
making it more accurate and effective. The convolutional neural network
long short-term memory and autoencoder-long short-term memory models
have shown promise as tools for achieving this goal, with the autoencoder
model standing out for its ability to identify internal relationships in data.
The article also discusses potential clinical applications of these algorithms
and directions for future research.
