Automated Polyp Segmentation in Colonoscopy Images

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.