Billah Mustain, Waheed Sajjad
Department of Information and Communication Technology (ICT), Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
Biomed Eng Lett. 2017 Sep 7;8(1):69-75. doi: 10.1007/s13534-017-0048-x. eCollection 2018 Feb.
Gastrointestinal polyps are treated as the precursors of cancer development. So, possibility of cancers can be reduced at a great extent by early detection and removal of polyps. The most used diagnostic modality for gastrointestinal polyps is video endoscopy. But, as an operator dependant procedure, several human factors can lead to miss detection of polyps. In this peper, an improved computer aided polyp detection method has been proposed. Proposed improved method can reduce polyp miss detection rate and assists doctors in finding the most important regions to pay attention. Color wavelet features and convolutional neural network features are extracted from endoscopic images, which are used for training a support vector machine. Then a target endoscopic image will be given to the classifier as input in order to find whether it contains any polyp or not. If polyp is found, it will be marked automatically. Experiment shows that, color wavelet features and convolutional neural network features together construct a highly representative of endoscopic polyp images. Evaluations on standard public databases show that, proposed system outperforms state-of-the-art methods, gaining accuracy of 98.34%, sensitivity of 98.67% and specificity of 98.23%. In this paper, the strength of color wavelet features and power of convolutional neural network features are combined. Fusion of these two methodology and use of support vector machine results in an improved method for gastrointestinal polyp detection. An analysis of ROC reveals that, proposed method can be used for polyp detection purposes with greater accuracy than state-of-the-art methods.
胃肠道息肉被视为癌症发展的前兆。因此，通过早期检测和切除息肉，可在很大程度上降低患癌的可能性。用于胃肠道息肉的最常用诊断方法是视频内窥镜检查。但是，作为一种依赖操作人员的程序，一些人为因素可能导致息肉漏检。在本文中，提出了一种改进的计算机辅助息肉检测方法。所提出的改进方法可以降低息肉漏检率，并协助医生找到最重要的需关注区域。从内窥镜图像中提取颜色小波特征和卷积神经网络特征，用于训练支持向量机。然后，将目标内窥镜图像作为输入提供给分类器，以确定其是否包含任何息肉。如果发现息肉，将自动进行标记。实验表明，颜色小波特征和卷积神经网络特征共同构成了内窥镜息肉图像的高度代表性特征。在标准公共数据库上的评估表明，所提出的系统优于现有方法，准确率达到98.34%，灵敏度为98.67%，特异性为98.23%。在本文中，结合了颜色小波特征的优势和卷积神经网络特征的强大功能。这两种方法的融合以及支持向量机的使用产生了一种改进的胃肠道息肉检测方法。ROC分析表明，所提出的方法可用于息肉检测，其准确性高于现有方法。
