  This paper proposes to use Fast Fourier Transformation-based U-Net (a refined
fully convolutional networks) and perform image convolution in neural networks.
Leveraging the Fast Fourier Transformation, it reduces the image convolution
costs involved in the Convolutional Neural Networks (CNNs) and thus reduces the
overall computational costs. The proposed model identifies the object
information from the images. We apply the Fast Fourier transform algorithm on
an image data set to obtain more accessible information about the image data,
before segmenting them through the U-Net architecture. More specifically, we
implement the FFT-based convolutional neural network to improve the training
time of the network. The proposed approach was applied to publicly available
Broad Bioimage Benchmark Collection (BBBC) dataset. Our model demonstrated
improvement in training time during convolution from $600-700$ ms/step to
$400-500$ ms/step. We evaluated the accuracy of our model using Intersection
over Union (IoU) metric showing significant improvements.
