School of Mathematics, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Guangdong Province Key Laboratory of Computational Science, Sun Yat-Sen University, Guangzhou, Guangdong, China.
PLoS One. 2024 Oct 11;19(10):e0302800. doi: 10.1371/journal.pone.0302800. eCollection 2024.
Among the most common cancers, colorectal cancer (CRC) has a high death rate. The best way to screen for colorectal cancer (CRC) is with a colonoscopy, which has been shown to lower the risk of the disease. As a result, Computer-aided polyp classification technique is applied to identify colorectal cancer. But visually categorizing polyps is difficult since different polyps have different lighting conditions. Different from previous works, this article presents Enhanced Scattering Wavelet Convolutional Neural Network (ESWCNN), a polyp classification technique that combines Convolutional Neural Network (CNN) and Scattering Wavelet Transform (SWT) to improve polyp classification performance. This method concatenates simultaneously learnable image filters and wavelet filters on each input channel. The scattering wavelet filters can extract common spectral features with various scales and orientations, while the learnable filters can capture image spatial features that wavelet filters may miss. A network architecture for ESWCNN is designed based on these principles and trained and tested using colonoscopy datasets (two public datasets and one private dataset). An n-fold cross-validation experiment was conducted for three classes (adenoma, hyperplastic, serrated) achieving a classification accuracy of 96.4%, and 94.8% accuracy in two-class polyp classification (positive and negative). In the three-class classification, correct classification rates of 96.2% for adenomas, 98.71% for hyperplastic polyps, and 97.9% for serrated polyps were achieved. The proposed method in the two-class experiment reached an average sensitivity of 96.7% with 93.1% specificity. Furthermore, we compare the performance of our model with the state-of-the-art general classification models and commonly used CNNs. Six end-to-end models based on CNNs were trained using 2 dataset of video sequences. The experimental results demonstrate that the proposed ESWCNN method can effectively classify polyps with higher accuracy and efficacy compared to the state-of-the-art CNN models. These findings can provide guidance for future research in polyp classification.
在最常见的癌症中，结直肠癌（CRC）死亡率很高。筛查结直肠癌（CRC）的最佳方法是结肠镜检查，它已被证明可以降低这种疾病的风险。因此，计算机辅助息肉分类技术被应用于识别结直肠癌。但由于不同的息肉有不同的光照条件，因此肉眼对息肉进行分类是很困难的。与以前的工作不同，本文提出了增强散射小波卷积神经网络（ESWCNN），这是一种将卷积神经网络（CNN）和散射小波变换（SWT）结合起来提高息肉分类性能的息肉分类技术。该方法在每个输入通道上同时学习图像滤波器和小波滤波器。散射小波滤波器可以提取具有不同尺度和方向的常见光谱特征，而可学习滤波器可以捕获小波滤波器可能错过的图像空间特征。基于这些原理设计了 ESWCNN 的网络架构，并使用结肠镜检查数据集（两个公共数据集和一个私有数据集）对其进行了训练和测试。进行了 n 折交叉验证实验，对三个类别（腺瘤、增生、锯齿状）进行分类，准确率达到 96.4%，对两类息肉（阳性和阴性）进行分类，准确率达到 94.8%。在三类分类中，腺瘤的正确分类率为 96.2%，增生性息肉的正确分类率为 98.71%，锯齿状息肉的正确分类率为 97.9%。在二类实验中，该方法的平均灵敏度为 96.7%，特异性为 93.1%。此外，我们将我们的模型与最先进的通用分类模型和常用的 CNN 进行了性能比较。使用两个视频序列数据集训练了基于 CNN 的六个端到端模型。实验结果表明，与最先进的 CNN 模型相比，所提出的 ESWCNN 方法可以更有效地分类息肉，具有更高的准确性和效果。这些发现可以为未来的息肉分类研究提供指导。
