Automated Polyp Segmentation in Colonoscopy Images
Swagat Ranjit Jian Zhang Bijaya B. Karki
It is important to find the polyps in a human system that helps to prevent
cancer during medical diagnosis. This research discusses using a dilated
convolution module along with a criss cross attention-based network to segment
polyps from the endoscopic images of the colon. To gather the context
information of all pixels in an image more efficiently, criss-cross attention
module has played a vital role. In order to extract maximum information from
dataset, data augmentation techniques are employed in the dataset. Rotations,
flips, scaling, and contrast along with varying learning rates were implemented
to make a better model. Global average pooling was applied over ResNet50 that
helped to store the important details of encoder. In our experiment, the
proposed architecture's performance was compared with existing models like
U-Net, DeepLabV3, PraNet. This architecture outperformed other models on the
subset of dataset which has irregular polyp shapes. The combination of dilated
convolution module, RCCA, and global average pooling was found to be effective
for irregular shapes. Our architecture demonstrates an enhancement, with an
average improvement of 3.75% across all metrics when compared to existing
models.