Colorectal cancer contributes significantly to cancer-related mortality.
Timely identification and elimination of polyps through colonoscopy screening
is crucial in order to decrease mortality rates. Accurately detecting polyps in
colonoscopy images is difficult because of the differences in characteristics
such as size, shape, texture, and similarity to surrounding tissues. Current
deep-learning methods often face difficulties in capturing long-range
connections necessary for segmentation. This research presents BetterNet, a
convolutional neural network (CNN) architecture that combines residual learning
and attention methods to enhance the accuracy of polyp segmentation. The
primary characteristics encompass (1) a residual decoder architecture that
facilitates efficient gradient propagation and integration of multiscale
features. (2) channel and spatial attention blocks within the decoder block to
concentrate the learning process on the relevant areas of polyp regions. (3)
Achieving state-of-the-art performance on polyp segmentation benchmarks while
still ensuring computational efficiency. (4) Thorough ablation tests have been
conducted to confirm the influence of architectural components. (5) The model
code has been made available as open-source for further contribution. Extensive
evaluations conducted on datasets such as Kvasir-SEG, CVC ClinicDB, Endoscene,
EndoTect, and Kvasir-Sessile demonstrate that BetterNets outperforms current
SOTA models in terms of segmentation accuracy by significant margins. The
lightweight design enables real-time inference for various applications.
BetterNet shows promise in integrating computer-assisted diagnosis techniques
to enhance the detection of polyps and the early recognition of cancer. Link to
the code: this https URL

通过将残差学习和注意力方法结合的 BetterNet 卷积神经网络架构，有效增强息肉分割准确性，具有高效梯度传播、多尺度特征整合、关注关键区域学习过程、确保计算效率等特点，进一步优于现有的基准模型，为结直肠癌的早期识别和计算机辅助诊断提供新的可能。