Image Texture Based Hybrid Diagnostic Tool for Kidney Disease Classification

2018 ◽  
Vol 8 (9) ◽  
pp. 1899-1908
Author(s):  
P. Sreelatha ◽  
M. Ezhilarasi
Keyword(s):  
Diagnostic Tool ◽  
Classification Accuracy ◽  
Wavelet Coefficient ◽  
Region Of Interest ◽  
Principal Component ◽  
Disease Classification ◽  
Image Texture ◽  
Support Vector ◽  
Adaptive Wavelet ◽  
Medical Diagnostic

The identification of chronic medical conditions and its associated mortality has led to the emergence of less invasive methods for medical diagnostic imaging. This work proposes a Computer Aided Diagnostic tool useful in automatic classification of kidney images as normal, simple cysts, kidney stones and the less investigated complex cystic renal cell carcinoma. The first part of the work investigates an effective despeckling algorithm with a proposed adaptive wavelet based denoising technique. Encouraging increased PSNR values ranging from 15 dB to 24 dB were obtained. Second part of work suggests a set of wavelet coefficient based feature set which showed a classification accuracy of 92.2%, better by 20.3% to 0.8% against existing methods. The final part of the work to develop a complete tool for kidney image classification combines the proposed wavelet based features with three existing statistical based feature sets yielded a classification accuracy of 96.9%. The suggested features were extracted from the region of interest from an image set. A reduced feature set of 18 from the original size of 163 was obtained using principal component analysis and applied for training a support vector machine classifier.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals CLASSIFICATION AND RECOGNITION OF TOMB INFORMATION IN HYPERSPECTRAL IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 411-416
Author(s):  
M. Gu ◽  
S. Lyu ◽  
M. Hou ◽  
S. Ma ◽  
Z. Gao ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Processing Time ◽  
Hyperspectral Image ◽  
Research Result ◽  
Principal Component ◽  
Shanxi Province ◽  
Support Vector ◽  
Archaeological Research ◽  
Naked Eye ◽  
Archaeological Work

There are a large number of materials with important historical information in ancient tombs. However, in many cases, these substances could become obscure and indistinguishable by human naked eye or true colour camera. In order to classify and identify materials in ancient tomb effectively, this paper applied hyperspectral imaging technology to archaeological research of ancient tomb in Shanxi province. Firstly, the feature bands including the main information at the bottom of the ancient tomb are selected by the Principal Component Analysis (PCA) transformation to realize the data dimension. Then, the image classification was performed using Support Vector Machine (SVM) based on feature bands. Finally, the material at the bottom of ancient tomb is identified by spectral analysis and spectral matching. The results show that SVM based on feature bands can not only ensure the classification accuracy, but also shorten the data processing time and improve the classification efficiency. In the material identification, it is found that the same matter identified in the visible light is actually two different substances. This research result provides a new reference and research idea for archaeological work.

Kinematic Gait Patterns in Competitive and Recreational Runners

2017 ◽  
Vol 33 (4) ◽  
pp. 268-276 ◽  
Author(s):  
Christian A. Clermont ◽  
Sean T. Osis ◽  
Angkoon Phinyomark ◽  
Reed Ferber
Keyword(s):  
Classification Accuracy ◽  
Three Dimensional ◽  
Principal Component ◽  
Treadmill Running ◽  
Support Vector ◽  
Gait Kinematics ◽  
Ankle Eversion ◽  
Machine Learning Approach ◽  
Recreational Runners ◽  
Time Point

Certain homogeneous running subgroups demonstrate distinct kinematic patterns in running; however, the running mechanics of competitive and recreational runners are not well understood. Therefore, the purpose of this study was to determine whether we could separate and classify competitive and recreational runners according to gait kinematics using multivariate analyses and a machine learning approach. Participants were allocated to the ‘competitive’ (n = 20) or ‘recreational’ group (n = 15) based on age, sex, and recent race performance. Three-dimensional (3D) kinematic data were collected during treadmill running at 2.7 m/s. A support vector machine (SVM) was used to determine if the groups were separable and classifiable based on kinematic time point variables as well as principal component (PC) scores. A cross-fold classification accuracy of 80% was found between groups using the top 5 ranked time point variables, and the groups could be separated with 100% cross-fold classification accuracy using the top 14 ranked PCs explaining 60.29% of the variance in the data. The features were primarily related to pelvic tilt, as well as knee flexion and ankle eversion in late stance. These results suggest that competitive and recreational runners have distinct, ‘typical’ running patterns that may help explain differences in injury mechanisms.

scholarly journals Differential Privacy Principal Component Analysis for Support Vector Machines

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuxian Huang ◽  
Geng Yang ◽  
Yahong Xu ◽  
Hao Zhou
Keyword(s):  
Principal Component Analysis ◽  
Classification Accuracy ◽  
Differential Privacy ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Support Vector ◽  
Data Sets ◽  
Vector Machines ◽  
Fast Classification

In big data era, massive and high-dimensional data is produced at all times, increasing the difficulty of analyzing and protecting data. In this paper, in order to realize dimensionality reduction and privacy protection of data, principal component analysis (PCA) and differential privacy (DP) are combined to handle these data. Moreover, support vector machine (SVM) is used to measure the availability of processed data in our paper. Specifically, we introduced differential privacy mechanisms at different stages of the algorithm PCA-SVM and obtained the algorithms DPPCA-SVM and PCADP-SVM. Both algorithms satisfy ε , 0 -DP while achieving fast classification. In addition, we evaluate the performance of two algorithms in terms of noise expectation and classification accuracy from the perspective of theoretical proof and experimental verification. To verify the performance of DPPCA-SVM, we also compare our DPPCA-SVM with other algorithms. Results show that DPPCA-SVM provides excellent utility for different data sets despite guaranteeing stricter privacy.

COMPUTING IMAGE TEXTURE BY ISOPATTERN

2014 ◽  
Vol 28 (05) ◽  
pp. 1454003
Author(s):  
S. MOHAMED MANSOOR ROOMI ◽  
R. RAJA ◽  
D. KALAIYARASI
Keyword(s):  
Texture Analysis ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Weighting Function ◽  
Texture Classification ◽  
Texture Feature ◽  
Region Of Interest ◽  
Image Texture ◽  
Textural Feature ◽  
Bit Plane

Texture is an important feature that aids in identifying objects of interest or region of interest irrespective of the source of the image. In this paper, a novel and simple isopattern-based texture feature is introduced. Spatial gray scale dependencies represented by bit plane is analyzed for specific patterns and are accumulated in bins. These are scaled by half-normal weighting function to provide isopattern texture feature. The ability of this texture feature in capturing textural variations of the images despite the presence of illumination, scale and rotation is demonstrated by conducting texture analysis on Brodatz, OuTex texture datasets and its classification accuracy on Kylberg dataset. The results of these two experimentation indicate that the proposed textural feature picks variation in texture significantly and has a better texture classification accuracy of 98.26% when compared with the state-of-the-art features like Gabor, GLCM and LBP.

A soft computing approach for diabetes disease classification

2016 ◽  
Vol 24 (4) ◽  
pp. 379-393 ◽  
Author(s):  
Mehrbakhsh Nilashi ◽  
Othman Bin Ibrahim ◽  
Abbas Mardani ◽  
Ali Ahani ◽  
Ahmad Jusoh
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Intelligent System ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Noise Removal ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Support Vector

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

scholarly journals Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

Molecules ◽  
2020 ◽  
Vol 25 (7) ◽  
pp. 1651 ◽  
Author(s):  
Xiulin Bai ◽  
Qinlin Xiao ◽  
Lei Zhou ◽  
Yu Tang ◽  
Yong He
Keyword(s):  
Sulfur Dioxide ◽  
Hyperspectral Imaging ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Imaging System ◽  
Principal Component ◽  
Support Vector ◽  
Svm Model ◽  
Fresh Cut ◽  
Potato Slices

Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky–Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.

scholarly journals MCI Detection Using Kernel Eigen-Relative-Power Features of EEG Signals

Actuators ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 152
Author(s):  
Yu-Tsung Hsiao ◽  
Chia-Fen Tsai ◽  
Chien-Te Wu ◽  
Thanh-Tung Trinh ◽  
Chun-Ying Lee ◽  
...  
Keyword(s):  
Early Detection ◽  
Classification Accuracy ◽  
Spectral Power ◽  
Principal Component ◽  
Classification Performance ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Relative Power ◽  
Linear Discriminant ◽  
Nonlinear Classifier

Classification between individuals with mild cognitive impairment (MCI) and healthy controls (HC) based on electroencephalography (EEG) has been considered a challenging task to be addressed for the purpose of its early detection. In this study, we proposed a novel EEG feature, the kernel eigen-relative-power (KERP) feature, for achieving high classification accuracy of MCI versus HC. First, we introduced the relative powers (RPs) between pairs of electrodes across 21 different subbands of 2-Hz width as the features, which have not yet been used in previous MCI-HC classification studies. Next, the Fisher’s class separability criterion was applied to determine the best electrode pairs (five electrodes) as well as the frequency subbands for extracting the most sensitive RP features. The kernel principal component analysis (kernel PCA) algorithm was further performed to extract a few more discriminating nonlinear principal components from the optimal RPs, and these components form a KERP feature vector. Results carried out on 51 participants (24 MCI and 27 HC) show that the newly introduced subband RP feature showed superior classification performance to commonly used spectral power features, including the band power, single-electrode relative power, and also the RP based on the conventional frequency bands. A high leave-one-participant-out cross-validation (LOPO-CV) classification accuracy 86.27% was achieved by the RP feature, using a simple linear discriminant analysis (LDA) classifier. Moreover, with the same classifier, the proposed KERP further improved the accuracy to 88.24%. Finally, cascading the KERP feature to a nonlinear classifier, the support vector machine (SVM), yields a high MCI-HC classification accuracy of 90.20% (sensitivity = 87.50% and specificity = 92.59%). The proposed method demonstrated a high accuracy and a high usability (only five electrodes are required), and therefore, has great potential to further develop an EEG-based computer-aided diagnosis system that can be applied for the early detection of MCI.

scholarly journals A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 259 ◽  
Author(s):  
Diana Toledo-Pérez ◽  
Miguel Martínez-Prado ◽  
Roberto Gómez-Loenzo ◽  
Wilfrido Paredes-García ◽  
Juvenal Rodríguez-Reséndiz
Keyword(s):  
Lower Limb ◽  
Classification Accuracy ◽  
Research Work ◽  
Principal Component ◽  
Support Vector ◽  
Semg Signal ◽  
Zero Crossings ◽  
The Difference ◽  
The Right

The number and position of sEMG electrodes have been studied extensively due to the need to improve the accuracy of the classification they carry out of the intention of movement. Nevertheless, increasing the number of channels used for this classification often increases their processing time as well. This research work contributes with a comparison of the classification accuracy based on the different number of sEMG signal channels (one to four) placed in the right lower limb of healthy subjects. The analysis is performed using Mean Absolute Values, Zero Crossings, Waveform Length, and Slope Sign Changes; these characteristics comprise the feature vector. The algorithm used for the classification is the Support Vector Machine after applying a Principal Component Analysis to the features. The results show that it is possible to reach more than 90% of classification accuracy by using 4 or 3 channels. Moreover, the difference obtained with 500 and 1000 samples, with 2, 3 and 4 channels, is not higher than 5%, which means that increasing the number of channels does not guarantee 100% precision in the classification.

A NOVEL IMAGE PROCESSING ALGORITHM TO AUTOMATICALLY EXTRACT AND CLASSIFY LIVER BEARING TUMORS USING PATTERN RECOGNITION AND FUZZY TRANSFORM

2021 ◽  
Author(s):  
Ghous Bakhsh Narejo ◽  
Ayesha Amir Siddiqi ◽  
Adnan Hashmi
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Morphological Processing ◽  
Disease Classification ◽  
Support Vector ◽  
Gray Level ◽  
Ct Image ◽  
Fuzzy Transformation ◽  
Occurrence Matrix

This study presents a novel liver disease classification method by applying pattern recognition technique to automatically segmented liver from the images of computed tomographic (CT) scans. The methodology comprises of disease classification by the extraction of textural features from focal liver region bearing tumors. Two types of liver textures are investigated in this study for classification accuracy judgement. First, original liver texture is considered for feature extraction. Second, liver is used for feature extraction. The CT image dataset comprises 308 liver samples with 193 samples of malignant tumor and 115 samples of benign tumor. The entire liver tissue bearing tumor is segmented from the CT image automatically in the pre-processing stage using fuzzy transformation function and morphological processing. Four sets of textural feature matrices are applied to the liver for feature extraction. Gray level co-occurrence matrix (GLCM), standard deviation gray level co-occurrence matrix (SDGLCM), seven-moment matrix (7MM) and seven-moment gray level co-occurrence matrix (7MGLCM) are the combinational feature matrices applied to classify the liver as malignant or benign using support vector machines (SVMs). The best classification accuracy is achieved for original liver texture by 7MGLCM, which is 97% with AUC[Formula: see text]0.99 for training dataset and 97.8% with AUC[Formula: see text]1 for test dataset.

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