  Purpose Medical imaging diagnosis faces challenges, including low-resolution
images due to machine artifacts and patient movement. This paper presents the
Frequency-Guided U-Net (GFNet), a novel approach for medical image segmentation
that addresses challenges associated with low-resolution images and inefficient
feature extraction. Approach In response to challenges related to computational
cost and complexity in feature extraction, our approach introduces the
Attention Filter Gate. Departing from traditional spatial domain learning, our
model operates in the frequency domain using FFT. A strategically placed
weighted learnable matrix filters feature, reducing computational costs. FFT is
integrated between up-sampling and down-sampling, mitigating issues of
throughput, latency, FLOP, and enhancing feature extraction. Results
Experimental outcomes shed light on model performance. The Attention Filter
Gate, a pivotal component of GFNet, achieves competitive segmentation accuracy
(Mean Dice: 0.8366, Mean IoU: 0.7962). Comparatively, the Attention Gate model
surpasses others, with a Mean Dice of 0.9107 and a Mean IoU of 0.8685. The
widely-used U-Net baseline demonstrates satisfactory performance (Mean Dice:
0.8680, Mean IoU: 0.8268). Conclusion his work introduces GFNet as an efficient
and accurate method for medical image segmentation. By leveraging the frequency
domain and attention filter gates, GFNet addresses key challenges of
information loss, computational cost, and feature extraction limitations. This
novel approach offers potential advancements for computer-aided diagnosis and
other healthcare applications. Keywords: Medical Segmentation, Neural Networks,
