Background and Objective

The increased incidence of colorectal cancer (CRC) and its mortality rate have attracted interest in the use of artificial intelligence (AI) based computer-aided diagnosis (CAD) tools to detect polyps at an early stage. Although these CAD tools have thus far achieved a good accuracy level to detect polyps, they still have room to improve further (e.g. sensitivity). Therefore, a new CAD tool is developed in this study to detect colonic polyps accurately.

Methods

In this paper, we propose a novel approach to distinguish colonic polyps by integrating several techniques, including a modified deep residual network, principal component analysis and AdaBoost ensemble learning. A powerful deep residual network architecture, ResNet-50, was investigated to reduce the computational time by altering its architecture. To keep the interference to a minimum, median filter, image thresholding, contrast enhancement, and normalisation techniques were exploited on the endoscopic images to train the classification model. Three publicly available datasets, i.e., Kvasir, ETIS-LaribPolypDB, and CVC-ClinicDB, were merged to train the model, which included images with and without polyps.

Results

The proposed approach trained with a combination of three datasets achieved Matthews Correlation Coefficient (MCC) of 0.9819 with accuracy, sensitivity, precision, and specificity of 99.10%, 98.82%, 99.37%, and 99.38%, respectively.

Conclusions

These results show that our method could repeatedly classify endoscopic images automatically and could be used to effectively develop computer-aided diagnostic tools for early CRC detection.

中文翻译：

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结合改进的深度残差卷积神经网络和集成学习方法自动进行结肠息肉检测

背景与目的

大肠癌（CRC）的发病率及其死亡率的上升已经引起了人们对基于人工智能（AI）的计算机辅助诊断（CAD）工具在早期发现息肉的兴趣。尽管到目前为止这些CAD工具已经达到了检测息肉的良好准确性，但它们仍有进一步改进的空间（例如，灵敏度）。因此，在这项研究中开发了一种新的CAD工具，可以准确地检测结肠息肉。

方法

在本文中，我们提出了一种通过整合多种技术来区分结肠息肉的新方法，其中包括改良的深度残差网络，主成分分析和AdaBoost集成学习。研究了功能强大的深度残差网络体系结构ResNet-50，以通过更改其体系结构来减少计算时间。为了使干扰最小，对内窥镜图像采用中值滤波，图像阈值化，对比度增强和归一化技术来训练分类模型。合并了三个公开可用的数据集，即Kvasir，ETIS-LaribPolypDB和CVC-ClinicDB，以训练模型，其中包括有息肉和无息肉的图像。

结果

所提出的方法结合了三个数据集进行训练，获得的马修斯相关系数（MCC）为0.9819，准确度，灵敏度，精确度和特异性分别为99.10％，98.82％，99.37％和99.38％。

结论

这些结果表明，我们的方法可以自动对内窥镜图像进行重复分类，可用于有效开发用于早期CRC检测的计算机辅助诊断工具。