DELINEATION AND CLASSIFICATION OF LIVER CANCER USING LEVEL SET METHOD IN CT IMAGES

2017 ◽  
Vol 29 (06) ◽  
pp. 1750047
Author(s):  
Amita Das ◽  
S. S. Panda ◽  
Sukanta Sabut
Keyword(s):  
Machine Learning ◽  
Level Set ◽  
Texture Classification ◽  
Ct Images ◽  
Experimental Result ◽  
Support Vector ◽  
Svm Classifier ◽  
Correct Prediction ◽  
Liver Cancers

The paper proposes a modified approach of delineation and classification of two different types of liver cancers viz. Hepatocellular Carcinoma (HCC) and Metastatic Carcinoma (MET) from different slices of computed tomography (CT) scans images. A combined framework of reorganization and extraction of region of interest (ROI), texture feature extraction followed by texture classification by different machine learning approaches has been presented. Initially, adaptive thresholding has been applied to segment the liver region from CT images. Level set algorithm has been used for detecting the region of cancer tissues. In the classification stage, the delineated output lesions have been extracted with 38 features to build up the dataset. Two machine learning classifiers, support vector machine (SVM) and random forest (RF), have been used to train the dataset for correct prediction of cancer classes. Ten-fold cross-validation has been used to evaluate the performance of two classifiers. The efficiency of the proposed algorithm is tested in terms of accuracy, where the RF classifier achieved a higher accuracy of 95% compared to SVM classifier of 87%. The experimental result proves the superiority of RF classifier compared to SVM classifier with level-set features.

Classification of Diffusion Tensor Metrics for the Diagnosis of a Myelopathic Cord Using Machine Learning

2018 ◽  
Vol 28 (02) ◽  
pp. 1750036 ◽  
Author(s):  
Shuqiang Wang ◽  
Yong Hu ◽  
Yanyan Shen ◽  
Hanxiong Li
Keyword(s):  
Machine Learning ◽  
Surgical Planning ◽  
Diffusion Tensor ◽  
Mean Value ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Control Groups ◽  
Diffusion Tensor Imaging Dti

In this study, we propose an automated framework that combines diffusion tensor imaging (DTI) metrics with machine learning algorithms to accurately classify control groups and groups with cervical spondylotic myelopathy (CSM) in the spinal cord. The comparison between selected voxel-based classification and mean value-based classification were performed. A support vector machine (SVM) classifier using a selected voxel-based dataset produced an accuracy of 95.73%, sensitivity of 93.41% and specificity of 98.64%. The efficacy of each index of diffusion for classification was also evaluated. Using the proposed approach, myelopathic areas in CSM are detected to provide an accurate reference to assist spine surgeons in surgical planning in complicated cases.

scholarly journals Texture classification of fabric defects using machine learning

2020 ◽  
Vol 10 (4) ◽  
pp. 4390
Author(s):  
Yassine Ben Salem ◽  
Mohamed Naceur Abdelkrim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Texture Classification ◽  
Support Vector ◽  
Defect Classification ◽  
Analysis Method ◽  
Fabric Defect ◽  
Novel Algorithm

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

scholarly journals Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

2020 ◽  
Vol 44 (6) ◽  
pp. 415-427
Author(s):  
Jung Ho Yang ◽  
Jae Hyeon Park ◽  
Seong-Ho Jang ◽  
Jaesung Cho
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Osteoarthritis ◽  
Logistic Regression Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Method ◽  
Learning Method

Objective To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.Methods A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.Results In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.Conclusion The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

scholarly journals Classification of Liver Tumor on Ct Images using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11336-11338
Keyword(s):  
Machine Learning ◽  
Liver Tumor ◽  
Region Of Interest ◽  
Region Growing ◽  
Ct Images ◽  
Svm Classifier ◽  
Intensity Level ◽  
Image Region ◽  
Occurrence Matrix

Liver tumor is one of the most severe types of cancerous diseases which is responsible for the death of many patients. CT Liver tumor images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from CT images because of several anatomical changes in different patients. The tumor is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from CT image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed SVM classifier system. Performance analysis of SVM classification of CT liver tumor image is studied. This will be useful for physician in better automatic diagnosis of liver tumor from CT images.

scholarly journals A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 795
Author(s):  
Dongbing Yu ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Green Tea ◽  
Electronic Nose ◽  
Classification Performance ◽  
Small Sample ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrimination Ability ◽  
Fine Grained

Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

A simplified framework for the detection of intracranial hemorrhage in CT brain images using deep learning

2021 ◽  
Vol 17 ◽  
Author(s):  
Praveen K ◽  
Sasikala M ◽  
Janani A ◽  
Nijisha Shajil ◽  
Hari Nishanthi V
Keyword(s):  
Deep Learning ◽  
Intracranial Hemorrhage ◽  
Feature Selection Method ◽  
Ct Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Brain Images ◽  
Ct Brain ◽  
Ct Brain Images

Background: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is utmost important to avoid untoward incidents that may even lead to death.Hence, this presented work leverages the ability of a pretrained deep convolutional neural network (CNN) for the detection of ICH in computed tomography (CT) brain images. Methods: Different frameworks have been analyzed for their effectiveness for the classification of CT brain images into hemorrhage or non-hemorrhage conditions. All these frameworks were investigated on CQ500 dataset. Furthermore, an exclusive preprocessing pipeline was designed for both normal and ICH CT images. Firstly, a framework involving the pretrained deep CNN, AlexNet, has been exploited for both feature extraction and classification using the transfer learning method, secondly, a modified AlexNet-Support vector machine (SVM) classifier is explored and finally, a feature selection method, Principal Component Analysis (PCA) has been introduced in the AlexNet-SVM classifier model and its efficacy is explored.These models were trained and tested on two different sets of CT images, one containing the original images without preprocessing and another set consisting of preprocessed images. Results: The modified AlexNet-SVM classifier has shown an improved performance in comparison to the other investigated frameworks and has achieved a classification accuracy of 99.86%, sensitivity and specificity of 0.9986 for the detection of ICH in brain CT images. Conclusion: This research has given an overview of a simple and efficient framework for the classification of hemorrhage and non-hemorrhage images. Also, the proposed simplified deep learning framework manifests its ability as a screening tool to assist the radiological trainees for the accurate detection of ICH.

Gesture Classification of Surface Electromyography Signals Using Machine Learning Algorithms for Hand Prosthetics

2021 ◽  
Vol 11 (12) ◽  
pp. 3141-3152
Author(s):  
N. Subhashini ◽  
A. Kandaswamy
Keyword(s):  
Machine Learning ◽  
Surface Electromyography ◽  
Learning Algorithms ◽  
Ensemble Classifier ◽  
Correlation Factor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Movement Classification

The actions of humans executed by their hands play a remarkable part in controlling and handling variety of objects in their daily life activities. The effect of losing or degradation in the functioning of one hand has a greater influence in bringing down the regular activity. Hence the design of prosthetic hands which assists the individuals to enhance their regular activity seems a better remedy in this new era. This paper puts forward a classification framework using machine learning algorithms for classifying hand gesture signals. The surface electromyography (sEMG) dataset acquired for 9 wrist movements of publicly available database are utilized to identify the potential biomarkers for classification and in evaluating the efficacy of the proposed algorithm. The statistical and time domain features of the sEMG signals from 27 intact subjects and 11 trans-radial amputated subjects are extracted and the optimal features are determined implementing the feature selection approach based on correlation factor. The classifiers performance of machine learning algorithms namely support vector machine (SVM), Naïve bayes (NB) and Ensemble classifier are evaluated. The experimental results highlight that the SVM classifier can yield the maximum accuracy movement classification of 99.6% for intact and 97.56% for trans-amputee subjects. The proposed approach offers better accuracy and sensitivity compared to other approaches that have used the sEMG dataset for movement classification.

scholarly journals Classification of Child Items in a Gold Tree using Support Vector Machine Classifier

2019 ◽  
Vol 8 (4) ◽  
pp. 3208-3216
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Main Application ◽  
Novel Approach

Sorting of images has been a challenge in Machine Learning Algorithms over the years. Various algorithms have been proposed to sort an image but none of them are able to sort the image clearly. The drawback of the existing systems is that the sorted image is not clearly identified. So, to overcome this drawback we have proposed a novel approach to sort the children of a tree and match them with the existing designs. The images will be sorted on the basis of the class of the image. The images are taken from the image and manual binning of those images are done. Then the images are trained and tested. GLCM feature is extracted from the trained and tested images which are later on fed to the SVM classifier. The classification of image is then done with the help of SVM classifier. Around 7000 images are trained on SVM and used for classification. More than 300 different classes have been created in the database for comparison. Realtime images of child items are captured and fed to the SVM for classifying. The main application of this image is the use in distinguishing the designs in the ornaments. The various parts of the ornaments can be differentiated clearly. Thus, the proposed method is precise as compared to the existing methods.

scholarly journals Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

2020 ◽  
Vol 10 (8) ◽  
pp. 562
Author(s):  
Yingying Guo ◽  
Jianfeng Qiu ◽  
Weizhao Lu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Structural Changes ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Healthy Controls

Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (p < 0.001 tested with 1000-times permutation). The results demonstrated evidence of hippocampal and amygdaloid structural changes in schizophrenia patients, and also suggested that morphological features from the amygdaloid and hippocampal subregions could be used by machine learning algorithms for the classification of schizophrenia.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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