Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection
Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection
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DOI：
10.1002/jemt.23447
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发表时间：
2020-01-27
影响因子：
2.5
通讯作者：
Tariq, Usman
中科院分区：
文献类型：
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作者：
Majid, Abdul;Khan, Muhammad Attique;Tariq, Usman
通过无线胶囊内窥镜（WCE）自动检测和分类胃感染（如溃疡、息肉、食管炎和出血）仍然是一个关键挑战。医生可以通过使用计算机辅助诊断（CAD）系统来识别这些内窥镜疾病。本文提出了一种基于多类型特征提取、融合和鲁棒性特征选择的全自动化胃感染识别系统。执行五个关键步骤-数据库创建，手工和卷积神经网络（CNN）深度特征提取，提取特征的融合，使用遗传算法（GA）选择最佳特征，以及识别。在特征提取步骤中，分别提取离散余弦变换、离散小波变换强颜色特征和基于vgg16的CNN特征。然后，通过简单的数组拼接对这些特征进行融合，并进行遗传算法，基于k -最近邻适应度函数选择最佳特征。最后，将选出的最佳特征提供给集成分类器进行胃疾病的识别。使用kvasir、CVC-ClinicDB、Private和ETIS-LaribPolypDB四种类型的胃感染，如溃疡、息肉、食管炎和出血，建立了一个数据库。使用该数据库，与现有方法相比，该方法具有更好的性能，准确率达到96.5%。
Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) is still a key challenge. Doctors can identify these endoscopic diseases by using the computer-aided diagnostic (CAD) systems. In this article, a new fully automated system is proposed for the recognition of gastric infections through multi-type features extraction, fusion, and robust features selection. Five key steps are performed-database creation, handcrafted and convolutional neural network (CNN) deep features extraction, a fusion of extracted features, selection of best features using a genetic algorithm (GA), and recognition. In the features extraction step, discrete cosine transform, discrete wavelet transform strong color feature, and VGG16-based CNN features are extracted. Later, these features are fused by simple array concatenation and GA is performed through which best features are selected based on K-Nearest Neighbor fitness function. In the last, best selected features are provided to Ensemble classifier for recognition of gastric diseases. A database is prepared using four datasets-Kvasir, CVC-ClinicDB, Private, and ETIS-LaribPolypDB with four types of gastric infections such as ulcer, polyp, esophagitis, and bleeding. Using this database, proposed technique performs better as compared to existing methods and achieves an accuracy of 96.5%.