Enhanced VGG-UNet for the Deep Segmentation of Fungi Microscopic Images

Image segmentation is an essential process in fungi microscopic image analysis that visually represents fungi morphology. Recent research indicates that deep learning excels in image segmentation for microscopy images. U-Net is a standard method for deep image segmentation, but challenges arise due to limited training data on fungi images. Researchers are modifying the U-Net architecture to address these challenges and enhance segmentation results. The modifications focus on the encoder component to improve feature extraction, skip connections to enhance spatial features, and data augmentation to expand data diversity. This study proposes an enhanced VGG-UNet (VU-Net) approach called Attention VGG-UNet (AVU-Net), which combines hard and soft attention using attention gates (AGs) and augmentation for fungi microscopy image segmentation. This study utilized several data augmentation methods to find the best result. By utilizing data augmentation, Focal loss, and the AdamW optimizer, AVU-Net shows improved segmentation results with better details and reaches a mIoU score of 0.833. The results show that VGG is the most significant enhancement for the model, while AGs significantly improve VU-Net. However, AVU-Net, which implements soft attention, becomes less effective when hard attention is added through data augmentation.