FSA-net: Frequency-based spatial-temporal attention network for robust liver CT classification

Key Points

• Improved classification accuracy of liver CT images was achieved using the frequency-based attention model, providing better insights into imaging.
• The model demonstrated a significant increase in performance metrics, achieving an accuracy boost of over 15% on varied datasets.
• Assessment utilized a deep learning approach focused on spatial and temporal attention, effectively capturing critical features in the images.
• These findings could enable better and faster diagnoses, highlighting the need for advanced networks in medical imaging.