Autism Disease Detection Using Transfer Learning Techniques: Performance
  Comparison Between Central Processing Unit vs Graphics Processing Unit
  Functions for Neural Networks
Mst Shapna Akter Hossain Shahriar Alfredo Cuzzocrea
Neural network approaches are machine learning methods that are widely used
in various domains, such as healthcare and cybersecurity. Neural networks are
especially renowned for their ability to deal with image datasets. During the
training process with images, various fundamental mathematical operations are
performed in the neural network. These operations include several algebraic and
mathematical functions, such as derivatives, convolutions, and matrix
inversions and transpositions. Such operations demand higher processing power
than what is typically required for regular computer usage. Since CPUs are
built with serial processing, they are not appropriate for handling large image
datasets. On the other hand, GPUs have parallel processing capabilities and can
provide higher speed. This paper utilizes advanced neural network techniques,
such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST
VGG16, and our proposed models, to compare CPU and GPU resources. We
implemented a system for classifying Autism disease using face images of
autistic and non-autistic children to compare performance during testing. We
used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and
Execution time. It was observed that GPU outperformed CPU in all tests
conducted. Moreover, the performance of the neural network models in terms of
accuracy increased on GPU compared to CPU.