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MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image
Chen Peng1, Zhiqin Qian1, Kunyu Wang1
1School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China.
Sensors (Basel, Switzerland)
|December 17, 2024
Summary
This study introduces MugenNet, a hybrid model combining Convolutional Neural Networks (CNNs) and Transformers for efficient colonic polyp image segmentation. MugenNet achieves optimal performance and high speed, aiding in early polyp detection.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Accurate colonic polyp segmentation is crucial for early detection and diagnosis.
- Convolutional Neural Networks (CNNs) offer segmentation capabilities but suffer from long training times.
- Transformers provide computational efficiency via self-attention but risk information loss.
Purpose of the Study:
- To hybridize CNNs and Transformers to leverage their complementary strengths.
- To develop an efficient and accurate model for colonic polyp image segmentation.
- To introduce MugenNet for enhanced early detection of colonic polyps.
Main Methods:
- Applied the hybridization principle to combine CNN and Transformer architectures.
- Developed and implemented the MugenNet model for colonic polyp image segmentation.
- Conducted comprehensive experiments comparing MugenNet against other CNN models on public datasets.
Main Results:
- MugenNet achieved optimal performance on the ETIS dataset with a mean Dice score of 0.714.
- The model demonstrated a high inference speed of 56 FPS.
- Ablation experiments confirmed the effectiveness of the MugenNet architecture.
Conclusions:
- The proposed hybridization method effectively combines CNN and Transformer advantages.
- MugenNet offers a superior approach for colonic polyp image segmentation.
- This work contributes a computationally efficient and accurate tool for early polyp detection.
