  This work introduces EffiSegNet, a novel segmentation framework leveraging
transfer learning with a pre-trained Convolutional Neural Network (CNN)
classifier as its backbone. Deviating from traditional architectures with a
symmetric U-shape, EffiSegNet simplifies the decoder and utilizes full-scale
feature fusion to minimize computational cost and the number of parameters. We
evaluated our model on the gastrointestinal polyp segmentation task using the
publicly available Kvasir-SEG dataset, achieving state-of-the-art results.
Specifically, the EffiSegNet-B4 network variant achieved an F1 score of 0.9552,
mean Dice (mDice) 0.9483, mean Intersection over Union (mIoU) 0.9056, Precision
0.9679, and Recall 0.9429 with a pre-trained backbone - to the best of our
knowledge, the highest reported scores in the literature for this dataset.
Additional training from scratch also demonstrated exceptional performance
compared to previous work, achieving an F1 score of 0.9286, mDice 0.9207, mIoU
0.8668, Precision 0.9311 and Recall 0.9262. These results underscore the
importance of a well-designed encoder in image segmentation networks and the
effectiveness of transfer learning approaches.
