EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION

In an era where information flow is moving at a rapid pace, image data processing is becoming increasingly important as technology advances, including in healthcare. Convolutional Neural Network (CNN) has been a common approach in image classification, but the larger the volume of data and the complexity of the task, the more expensive the computational cost of CNN. With the rapid growth in the amount of image data, efficiency in data processing is becoming increasingly important. In this study, the performance of neural network models using the convolution layer and Fourier transform layer in medical image data classification was compared. The results show that models with a Fourier transform layer tend to provide higher accuracy and better Area Under Curve (AUC) compared to models using a convolution layer. In addition, the model with the Fourier transform layer also shows faster execution time per epoch, which indicates efficiency in data processing. However, the convolution layer has an advantage in terms of model size, although it is not significantly different from the Fourier transform layer. In conclusion, the Fourier transform layer has an advantage in the classification of medical image data.