Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach

2016 ◽  
pp. 321-346
Author(s):  
Bhuvaneswari Chandran ◽  
P. Aruna ◽  
D. Loganathan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Lung Diseases ◽  
Hybrid Genetic Algorithm ◽  
Extraction Methods ◽  
Disease Classification ◽  
Filter Method ◽  
Svm Classifier ◽  
Genetic Approach ◽  
Feature Extraction Method

The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.

Lung Disease Classification by Novel Shape-Based Feature Extraction and New Hybrid Genetic Approach

2017 ◽  
pp. 1885-1910
Author(s):  
Bhuvaneswari Chandran ◽  
P. Aruna ◽  
D. Loganathan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Lung Diseases ◽  
Hybrid Genetic Algorithm ◽  
Extraction Methods ◽  
Disease Classification ◽  
Filter Method ◽  
Svm Classifier ◽  
Genetic Approach ◽  
Feature Extraction Method

The purpose of the chapter is to present a novel method to classify lung diseases from the computed tomography images which assist physicians in the diagnosis of lung diseases. The method is based on a new approach which combines a proposed M2 feature extraction method and a novel hybrid genetic approach with different types of classifiers. The feature extraction methods performed in this work are moment invariants, proposed multiscale filter method and proposed M2 feature extraction method. The essential features which are the results of the feature extraction technique are selected by the novel hybrid genetic algorithm feature selection algorithms. Classification is performed by the support vector machine, multilayer perceptron neural network and Bayes Net classifiers. The result obtained proves that the proposed technique is an efficient and robust method. The performance of the proposed M2 feature extraction with proposed hybrid GA and SVM classifier combination achieves maximum classification accuracy.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

scholarly journals Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks

2018 ◽  
Vol 10 (7) ◽  
pp. 1123 ◽  
Author(s):  
Yuhang Zhang ◽  
Hao Sun ◽  
Jiawei Zuo ◽  
Hongqi Wang ◽  
Guangluan Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Loss Function ◽  
Extraction Method ◽  
Generative Adversarial Networks ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Adversarial Networks ◽  
Aircraft Type

Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts’ keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.

A Hybrid Feature Extraction Method for Heart Disease Classification using ECG Signals

2021 ◽  
Author(s):  
Emir Akcin ◽  
Kemal Sami Isleyen ◽  
Enes Ozcan ◽  
Alaa Ali Hameed ◽  
Erdal Alimovski ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Heart Disease ◽  
Extraction Method ◽  
Disease Classification ◽  
Feature Extraction Method ◽  
Ecg Signals ◽  
Hybrid Feature Extraction

Wavelets-Based Feature Extraction for Texture Classification

2010 ◽  
Vol 97-101 ◽  
pp. 1273-1276 ◽  
Author(s):  
Gang Yu ◽  
Ying Zi Lin ◽  
Sagar Kamarthi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Energy Content ◽  
Texture Classification ◽  
Wavelet Coefficient ◽  
Discrimination Performance ◽  
Extraction Methods ◽  
Rotation Invariant ◽  
Scale Invariant ◽  
Feature Extraction Method

Texture classification is a necessary task in a wider variety of application areas such as manufacturing, textiles, and medicine. In this paper, we propose a novel wavelet-based feature extraction method for robust, scale invariant and rotation invariant texture classification. The method divides the 2-D wavelet coefficient matrices into 2-D clusters and then computes features from the energies inherent in these clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors are input to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to that obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.

scholarly journals Force-Invariant Improved Feature Extraction Method for Upper-Limb Prostheses of Transradial Amputees

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 843
Author(s):  
Md. Johirul Islam ◽  
Shamim Ahmad ◽  
Fahmida Haque ◽  
Mamun Bin Ibne Reaz ◽  
Mohammad Arif Sobhan Bhuiyan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Spatial Correlation ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Nonlinear Transformation ◽  
Computational Time ◽  
Feature Extraction Method ◽  
Invariant Feature ◽  
Upper Limb Prostheses

A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher pattern recognition performance, and significant improvements in accuracy, sensitivity, specificity, precision, and F1 score are found. In addition, the proposed method requires comparatively less computational time and memory, which makes it more robust than other well-known feature extraction methods.

scholarly journals From Complex System Analysis to Pattern Recognition: Experimental Assessment of an Unsupervised Feature Extraction Method Based on the Relevance Index Metrics

Computation ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 39 ◽  
Author(s):  
Laura Sani ◽  
Riccardo Pecori ◽  
Monica Mordonini ◽  
Stefano Cagnoni
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Extraction Method ◽  
System Analysis ◽  
Feature Space ◽  
Extraction Methods ◽  
Feature Extraction Method ◽  
Real World Datasets ◽  
Relevance Index ◽  
Unsupervised Feature Extraction

The so-called Relevance Index (RI) metrics are a set of recently-introduced indicators based on information theory principles that can be used to analyze complex systems by detecting the main interacting structures within them. Such structures can be described as subsets of the variables which describe the system status that are strongly statistically correlated with one another and mostly independent of the rest of the system. The goal of the work described in this paper is to apply the same principles to pattern recognition and check whether the RI metrics can also identify, in a high-dimensional feature space, attribute subsets from which it is possible to build new features which can be effectively used for classification. Preliminary results indicating that this is possible have been obtained using the RI metrics in a supervised way, i.e., by separately applying such metrics to homogeneous datasets comprising data instances which all belong to the same class, and iterating the procedure over all possible classes taken into consideration. In this work, we checked whether this would also be possible in a totally unsupervised way, i.e., by considering all data available at the same time, independently of the class to which they belong, under the hypothesis that the peculiarities of the variable sets that the RI metrics can identify correspond to the peculiarities by which data belonging to a certain class are distinguishable from data belonging to different classes. The results we obtained in experiments made with some publicly available real-world datasets show that, especially when coupled to tree-based classifiers, the performance of an RI metrics-based unsupervised feature extraction method can be comparable to or better than other classical supervised or unsupervised feature selection or extraction methods.

Study on Feature Extraction Methods Based on Motion Capture Data

2014 ◽  
Vol 926-930 ◽  
pp. 2114-2117
Author(s):  
Yong Dan Nie ◽  
Yan Zhang ◽  
Xian Mei Liu
Keyword(s):  
Feature Extraction ◽  
Motion Capture ◽  
Extraction Method ◽  
Data Retrieval ◽  
Extraction Methods ◽  
Motion Capture Data ◽  
Motion Feature ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Analysis Of Motion

By the analysis of motion Geometric features and Continuing feature that the motion capture data of the BVH format showed,Motion feature extraction method was proposed in this paper to preserve the motion original features in the maximum extent and marked motion data,improved the speed of motion data retrieval,and also provided a new method for rendering of motion characters in the virtual environment.

scholarly journals A Novel 2D Feature Extraction Method for Fingerprints Using Minutiae Points and Their Intersections

2017 ◽  
Vol 7 (5) ◽  
pp. 2547
Author(s):  
Nibras Ar Rakib ◽  
SM Zamshed Farhan ◽  
Md Mashrur Bari Sobhan ◽  
Jia Uddin ◽  
Arafat Habib
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Fingerprint Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Fingerprint Image ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
Recognition Systems ◽  
To Come

The field of biometrics has evolved tremendously for over the last century. Yet scientists are still continuing to come up with precise and efficient algorithms to facilitate automatic fingerprint recognition systems. Like other applications, an efficient feature extraction method plays an important role in fingerprint based recognition systems. This paper proposes a novel feature extraction method using minutiae points of a fingerprint image and their intersections. In this method, initially, it calculates the ridge ends and ridge bifurcations of each fingerprint image. And then, it estimates the minutiae points for the intersection of each ridge end and ridge bifurcation. In the experimental evaluation, we tested the extracted features of our proposed model using a support vector machine (SVM) classifier and experimental results show that the proposed method can accurately classify different fingerprint images.

scholarly journals A Face Recognition System using Directional Binary Code Algorithm and Multi-SVM

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1201-1208
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Binary Code ◽  
Recognition System ◽  
Extraction Methods ◽  
Recognition Algorithm ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Face Recognition System ◽  
The Face

Over past few years, face recognition technology plays an important function in the development of biometric identifier with less time consuming and computational overhead. Many researchers were put their effort to develop face recognition algorithm involves three distinct steps such as detection, unique faceprint creation and finally verification. Traditional Local binary pattern based face recognition system slow down the recognition speed, high computational complexity and does not give the directional data of the picture. In order to overcome the above limitation, a novel face recognition system is proposed by employing the advantage of Directional Binary Code (DBC) feature extraction method. The face images features are extracted from DBC are generally smoother than other feature extraction methods. The images with blur creation, pose changes, and illumination is applied and stored in the database. For blur creation various filters such as Average filter, Gaussian filter and Motion filter are used. By using Directional Binary Code method, the face is detected and extracted. Then the same algorithm is used for input images and with help of Multi-SVM classifier multiple images in the database is compared and shows the matched images. Finally, simulation result shows the implemented results in term of its recognition speed and computation complexity.

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