scholarly journals Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

2020 ◽  
Vol 12 (5) ◽  
pp. 1011-1023 ◽  
Author(s):  
Nilanjan Dey ◽  
V. Rajinikanth ◽  
Simon James Fong ◽  
M. Shamim Kaiser ◽  
Mufti Mahmud
Keyword(s):  
Computed Tomography ◽  
Social Group ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Computed Tomography Images ◽  
Kapur’S Entropy ◽  
Benchmark Datasets ◽  
Social Group Optimization ◽  
Novel Coronavirus ◽  
Segmentation Task

Abstract The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur’s entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.

scholarly journals Social-Group-Optimization Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

2020 ◽  
Author(s):  
Nilanjan Dey ◽  
V. Rajinikant ◽  
Simon James Fong ◽  
M. Shamim Kaiser ◽  
Mufti Mahmud
Keyword(s):  
Computed Tomography ◽  
Social Group ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Computed Tomography Images ◽  
Global Pandemic ◽  
Benchmark Datasets ◽  
Social Group Optimization ◽  
Novel Coronavirus ◽  
Segmentation Task

The Coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared as a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a Machine Learning based pipeline to detect the COVID-19 infection using the lung Computed Tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19 affected CTI using Social-Group-Optimization and Kapur’s Entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection and fusion to classify the infection. PCA based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test and validate four different classifiers namely Random Forest, k-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task and for the classification task the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose the ongoing COVID-19 infection.

scholarly journals Automated Vessel Segmentation in CT and CTA scans of the Lung Via Deep Neural Networks

2021 ◽  
Author(s):  
wenjun tan ◽  
luyu zhou ◽  
xiaoshuo li ◽  
xiaoyu yang ◽  
yufei chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Computed Tomography Angiography ◽  
Spatial Information ◽  
Discrete Distribution ◽  
Ground Truth ◽  
Pulmonary Vessel ◽  
Advantages And Disadvantages ◽  
Computed Tomography Images ◽  
Vascular Segmentation ◽  
Segmentation Task

Abstract Background: The distribution of pulmonary vessels in computed tomography images is important for diagnosing disease, formulating surgical plans and pulmonary research. However, there are many challenges of pulmonary vascular segmentation due to its characteristics of narrow and long pipes, discrete distribution and tree-like structure. With the development of deep learning and medical image processing, an automatic, accurate and fast segmentation algorithm of pulmonary blood vessels becomes possible. Methods: Based on the International Symposium on Image Computing and Digital Medicine 2020 challenge pulmonary vascular segmentation task, this paper objectively evaluates the performance of 12 different algorithms in chest computed tomography and computed tomography angiography. First, we present the annotated reference dataset including computed tomography and computed tomography angiography. Second, by analyzing the advantages and disadvantages of each team’s algorithm from 12 different institution, the reasons for some defects and improvements are summarized. Finally, we discuss the ways and methods to improve the results. Results: These methods were compared with the ground truth by the numerical results and the intuitive results from computed tomography and computed tomography angiography images. Most methods do an admirable job in pulmonary vascular extraction, with dice coefficients ranging from 0.70 to 0.85, and the dice coefficient for the top three methods are about 0.80. Conclusions: These results show that the methods which consider spatial information, fuse multi-scale feature map, or have an excellent post-processing are significant for further improving the accuracy of pulmonary vascular segmentation. Keywords: segmentation; pulmonary; vessel; U-Net; network; CT images; CTA

scholarly journals Epidemiological and Clinical Characteristics of 17 Hospitalized Patients with 2019 Novel Coronavirus Infections Outside Wuhan, China

2020 ◽  
Author(s):  
Jie Li ◽  
Shilin Li ◽  
Yurui Cai ◽  
Qin Liu ◽  
Xue Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Clinical Characteristics ◽  
National Standards ◽  
C Reactive Protein ◽  
Protein Levels ◽  
Reactive Protein ◽  
Computed Tomography Images ◽  
Younger Age ◽  
Novel Coronavirus ◽  
Epidemiologic Characteristics

SUMMARYAn increasing number of cases of novel coronavirus pneumonia (NCP) infected with 2019-nCoV have been identified in Wuhan and other cities in China, since December 2019. We analyzed data on the 17 confirmed cases in Dazhou to provide the epidemiologic characteristics of NCP outside Wuhan. Among them, 12 patients were still quarantined in the hospital, 5 patients were discharged NCP patients according to the national standards. Compared with non-discharged NCP patients, the discharged NCP patients had younger ages. Moreover, discharged NCP patients had higher heart rate, lymphocytes levels and monocytes levels than non-discharged NCP patients on admission to the hospital. Notably, all of 17 patients had abnormal increased C-reactive protein levels, and 16 patients had abnormal computed tomography images. This study provided some information that younger age, higher lymphocytes levels and monocytes levels at the diagnoses of 2019-nCoV may contributed to faster recovery and better therapeutic outcome.

scholarly journals A Transformer Fault Diagnosis Model Using an Optimal Hybrid Dissolved Gas Analysis Features Subset with Improved Social Group Optimization-Support Vector Machine Classifier

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1922 ◽  
Author(s):  
Jiake Fang ◽  
Hanbo Zheng ◽  
Jiefeng Liu ◽  
Junhui Zhao ◽  
Yiyi Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Social Group ◽  
Gas Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Diagnostic Model ◽  
Dissolved Gas ◽  
Feature Sets ◽  
Diagnostic Models ◽  
Social Group Optimization

Dissolved gas analysis (DGA) is widely used to detect the incipient fault of power transformers. However, the accuracy is greatly limited by selection of DGA features and performance of fault diagnostic model. This paper proposed a fault diagnostic method integrating feature selection and diagnostic model optimization. Firstly, this paper set up three feature sets with eight basic DGA gases, 28 DGA gas ratios and 36 hybrid DGA features, respectively. Then, to eliminate the interference of weak-relevant and irrelevant features, the genetic-algorithm-SVM-feature-screen (GA-SVM-FS) model was built to screen out the optimal hybrid DGA features subset (OHFS) from three feature sets. Next, using the OHFS as the input, the support vector machine (SVM) multi-classifier optimized by ISGOSVM (SVM classifier optimized by improved social group optimization) was built to diagnose fault types of transformers. Finally, the performance of OHFS and ISGOSVM diagnostic model was tested and compared with traditional DGA features and diagnostic models, respectively. The results show that the OHFS screened out is comprised of 14 features, including 12 gas ratios and two gases. The accuracy of OHFS is 3–30% higher than traditional DGA features, and the accuracy of ISGOSVM can increase by 3% to 14% compared with the SGOSVM (SVM classifier optimized by social group optimization), GASVM (SVM classifier optimized by genetic algorithm optimization), PSOSVM (SVM classifier optimized by particle swarm optimization), and SVM diagnostic models. The proposed approach integrating the OHFS with ISGOSVM achieves the highest accuracy of fault diagnose (92.86%).

scholarly journals A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on Cancer

2021 ◽  
Vol 9 (B) ◽  
pp. 1283-1289
Author(s):  
Jane Aurelia ◽  
Zuherman Rustam
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computed Tomography ◽  
Support Vector Machine ◽  
Support Vector ◽  
X Ray ◽  
Network Support ◽  
Computed Tomography Images ◽  
X Ray Computed

BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory. AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM. METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer. RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value. CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.

scholarly journals Liver tumor segmentation in CT volumes using an adversarial densely connected network

2019 ◽  
Vol 20 (S16) ◽  
Author(s):  
Lei Chen ◽  
Hong Song ◽  
Chi Wang ◽  
Yutao Cui ◽  
Jian Yang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Liver Tumor ◽  
Liver Tumors ◽  
Tumor Segmentation ◽  
Malignant Liver Tumor ◽  
Pixel Intensity ◽  
Computed Tomography Images ◽  
Adversarial Training ◽  
Liver Tumor Segmentation ◽  
Segmentation Task

Abstract Background Malignant liver tumor is one of the main causes of human death. In order to help physician better diagnose and make personalized treatment schemes, in clinical practice, it is often necessary to segment and visualize the liver tumor from abdominal computed tomography images. Due to the large number of slices in computed tomography sequence, developing an automatic and reliable segmentation method is very favored by physicians. However, because of the noise existed in the scan sequence and the similar pixel intensity of liver tumors with their surrounding tissues, besides, the size, position and shape of tumors also vary from one patient to another, automatic liver tumor segmentation is still a difficult task. Results We perform the proposed algorithm to the Liver Tumor Segmentation Challenge dataset and evaluate the segmentation results. Experimental results reveal that the proposed method achieved an average Dice score of 68.4% for tumor segmentation by using the designed network, and ASD, MSD, VOE and RVD improved from 27.8 to 21, 147 to 124, 0.52 to 0.46 and 0.69 to 0.73, respectively after performing adversarial training strategy, which proved the effectiveness of the proposed method. Conclusions The testing results show that the proposed method achieves improved performance, which corroborated the adversarial training based strategy can achieve more accurate and robustness results on liver tumor segmentation task.

Lungs nodule detection framework from computed tomography images using support vector machine

2019 ◽  
Vol 82 (8) ◽  
pp. 1256-1266 ◽  
Author(s):  
Sajid A. Khan ◽  
Muhammad Nazir ◽  
Muhammad A. Khan ◽  
Tanzila Saba ◽  
Kashif Javed ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Support Vector Machine ◽  
Support Vector ◽  
Computed Tomography Images ◽  
Nodule Detection

scholarly journals COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

2021 ◽  
Author(s):  
Asu Kumar Singh ◽  
Anupam Kumar ◽  
Mufti Mahmud ◽  
M Shamim Kaiser ◽  
Akshat Kishore
Keyword(s):  
Social Group ◽  
Intelligent System ◽  
Virus Disease ◽  
Support Vector ◽  
X Ray ◽  
Support Vector Classifier ◽  
Global Pandemic ◽  
Chest X Ray ◽  
Multi Class Classification ◽  
Social Group Optimization

AbstractA novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, there are almost 100 million cases worldwide with over 2 million confirmed deaths. Widespread testing is essential to reduce further spread of the disease, but due to a shortage of testing kits and limited supply, alternative testing methods are being evaluated. Recently researchers have found that chest X-Ray (CXR) images provide salient information about COVID-19. An intelligent system can help the radiologists to detect COVID-19 from these CXR images which can come in handy at remote locations in many developing nations. In this work, we propose a pipeline that uses CXR images to detect COVID-19 infection. The features from the CXR images were extracted and the relevant features were then selected using Hybrid Social Group Optimization algorithm. The selected features were then used to classify the CXR images using a number of classifiers. The proposed pipeline achieves a classification accuracy of 99.65% using support vector classifier, which outperforms other state-of-the-art deep learning algorithms for binary and multi-class classification.

Sign in / Sign up

Export Citation Format

Share Document