Image Retrieval Based on Deep Feature Extraction and Reduction with Improved CNN and PCA

Chen, Rongyu; Pan, Lili; Zhou, Yan; Lei, Qianhui

doi:10.32604/jihpp.2020.010472

Cited by 16 publications

(11 citation statements)

References 18 publications

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“…The improved performance after introducing PCA algorithm is distinctive, and the CNN + PCA + RF model is promising to be an effective computer-aided diagnostic tool for cervical cancer and greatly reduce the burden on pathologists, just as proved by recent publications. [31][32][33]…”

Section: Resultsmentioning

confidence: 99%

Whole slide cervical image classification based on convolutional neural network and random forest

Huang

Zheng-yin

et al. 2021

Int J Imaging Syst Tech

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Cervical cancer is a kind of common female malignancy ranking fourth for mortality worldwide. Traditional histopathological examination, an important diagnosis method of cervical cancer, is still manually performed by pathologists under the microscope, which is labor intensive and error‐prone. In this article, deep learning is used for whole slide cervical image (WSCI) analysis to explore an automatic and effective method for the diagnosis of cervical cancer. We combine convolutional neural network (CNN) and random forest (RF) classifier for whole slide cervical image classification. A new multilevel feature fusion strategy named Ensemble is used for features extraction from the features extracted by CNN. Ensemble that fuses features extracted by convolution layers with different depths in CNN together is capable of capturing fusional features which can describe patches at different levels from different convolutional layers. Principal component analysis (PCA) algorithm is introduced for feature reduction of the multilevel features. Our experiments are carried out on WSCI dataset which consists of a total of 163 WSCIs from 27 patients. We combine CNN + PCA + RF model and CNN + RF model, respectively, with four feature extraction strategies to conduct eight comparative experiments on the 163 WSCIs. Experimental results demonstrate that when using multilevel feature fusion strategy, the classification accuracy of the CNN + PCA + RF model reaches the highest to 99.39%. In addition, the CNN + PCA + RF model conducting the multilevel feature fusion strategy performs better than that conducting single‐level feature extraction strategies.

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Section: Resultsmentioning

confidence: 99%

Whole slide cervical image classification based on convolutional neural network and random forest

Huang

Zheng-yin

et al. 2021

Int J Imaging Syst Tech

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“…Need to be pre-processed. First remove the tags and symbols in the file, extract key information, and then use natural language processing to process some basic words through the methods of word segmentation, part-of-speech tagging and named entity recognition to extract normative data for features [14][15].…”

Section: Data Preprocessingmentioning

confidence: 99%

Answer Classification via Machine Learning in Community Question Answering

Jiang¹,

Zhang²,

Jia³

et al. 2021

Journal on Artificial Intelligence

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As a new type of knowledge sharing platform, the community question answer website realizes the acquisition and sharing of knowledge, and is loved and sought after by the majority of users. But for multi-answer questions, answer quality assessment becomes a challenge. The answer selection in CQA (Community Question Answer) was proposed as a challenge task in the SemEval competition, which gave a data set and proposed two subtasks. Task-A is to give a question (including short title and extended description) and its answers, and divide each answer into absolutely relevant (good), potentially relevant (potential) and bad or irrelevant (bad, dialog, non-English, other). Task-B is to give a YES/NO type question (including short title and extended description) and some answers. Based on the answer of the absolute correlation type (good), judge whether the answer to the whole question should be yes, no or uncertain. This paper first preprocesses this data set, and then uses natural language processing technology to perform word segmentation, part-of-speech tagging and named entity recognition on the data set, and then perform feature extraction on the preprocessed data set. Finally, SVM and random forest are used to classify on the basis of feature extraction, and the classification results are analyzed and compared. The experiments in this paper show that SVM and random forest methods have good results on the data set, and exceed the multi-classifier ensemble learning method and hierarchical classification method proposed by the predecessors.

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“…It can classify the input information according to hierarchical structure, and the input layer of a CNN can process multi-dimensional data. CNNs are able to learn spatial features and have achieved impressive results in many machine learning tasks [14,15]. Furthermore, CNNs have a good analysis effect on the status information of the communication payload, and many recent research results demonstrate its great potential.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

Deep Learning Anomaly Detection Based on Hierarchical Status-Connection Features in Networked Control Systems

Zhao¹,

Zeng²,

Chen³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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As networked control systems continue to be widely used in large-scale industrial productions, industrial cyber-attacks have become an inevitable problem that can cause serious damage to critical infrastructures. In practice, industrial intrusion detection has been widely acknowledged to detect abnormal communication behaviors. However, unlike traditional IT systems, networked control systems have their own communication characteristics due to specific industrial communication protocols. Thus, simple cyber-attack modeling is inadequate and impractical for high-efficiency intrusion detection because the characteristics of network control systems are less considered. Based on the status information and transmission connection in industrial communication data payloads, which can properly express the characteristics of industrial control logic, this paper associates industrial communication features with transmission connection payload and status payload. Furthermore, transmission connection features include device address, context, time, and packet length, while status features cover measurement, input, distributed state, control state, and more. After designing a convolutional neural network (CNN) and a long short-term memory network (LSTM) to extract status features and transmission connection features from industrial communication data, this paper proposes a hierarchical deep learning anomaly detection approach, which can integrate the advantages of CNN and LSTM to achieve high-efficiency detection. The experimental results clearly show that the proposed approach, having the advantages of strong detection capability and low false alarm rate, is a superior means of anomaly detection when compared to its peers.

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Image Retrieval Based on Deep Feature Extraction and Reduction with Improved CNN and PCA

Cited by 16 publications

References 18 publications

Whole slide cervical image classification based on convolutional neural network and random forest

Whole slide cervical image classification based on convolutional neural network and random forest

Answer Classification via Machine Learning in Community Question Answering

Deep Learning Anomaly Detection Based on Hierarchical Status-Connection Features in Networked Control Systems

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