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Neural network approaches are machine learning methods that are widely usedin various domains, such as healthcare and cybersecurity. Neural networks areespecially renowned for their ability to deal with image datasets. During thetraining process with images, various fundamental mathematical operations areperformed in the neural network. These operations include several algebraic andmathematical functions, such as derivatives, convolutions, and matrixinversions and transpositions. Such operations demand higher processing powerthan what is typically required for regular computer usage. Since CPUs arebuilt with serial processing, they are not appropriate for handling large imagedatasets. On the other hand, GPUs have parallel processing capabilities and canprovide higher speed. This paper utilizes advanced neural network techniques,such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOSTVGG16, and our proposed models, to compare CPU and GPU resources. Weimplemented a system for classifying Autism disease using face images ofautistic and non-autistic children to compare performance during testing. Weused evaluation matrices such as Accuracy, F1 score, Precision, Recall, andExecution time. It was observed that GPU outperformed CPU in all testsconducted. Moreover, the performance of the neural network models in terms ofaccuracy increased on GPU compared to CPU.

Autism Disease Detection Using Transfer Learning Techniques: Performance  Comparison Between Central Processing Unit vs Graphics Processing Unit  Functions for Neural Networks