scholarly journals Fast and Adaptive Detection of Pulmonary Nodules in Thoracic CT Images Using a Contextual Clustering Based Region Growing

2018 ◽  
Vol 7 (2.24) ◽  
pp. 106
Author(s):  
Bhakkiyalakshmi R ◽  
Ponnammal P ◽  
Srilekha M K ◽  
Abhishikt Sai .K
Keyword(s):  
Pulmonary Nodules ◽  
Region Of Interest ◽  
Region Growing ◽  
Support Vector ◽  
Adaptive Detection ◽  
Fuzzy Classifier ◽  
Thoracic Ct ◽  
Neuro Fuzzy ◽  
Feature Based ◽  
Better Than

For segmenting the Region of interest and for analyzing each area separately to locate whether pathologies present in it or not, we use segmentation process as the first step to diagnose lung image using ComputerAided Diagnosis.  In this paper, ROI is segmented by using supervised Contextual Clustering in addition to the Region growing algorithm. Accurate segmentation of the lungs from the chest volume is obtained from the Contextual clustering which is better than all other thresholding approaches that are simple. Initial Nodule Candidates can be detected and segmented effectively by contextual clustering which is considered to be the most effective approach when compared to the remaining approaches present.We combine rule-based filtering and a feature based support vector machine using which we can reduce the False-positives (FP) ,custom CNN, Alex net, neuro-fuzzy classifier. 

scholarly journals A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tsun-Kuo Lin
Keyword(s):  
Wavelet Transform ◽  
Region Growing ◽  
Support Vector ◽  
Manufacturing Processes ◽  
Dynamic Feature ◽  
Classification Methods ◽  
Multiple Objects ◽  
Multiple Object ◽  
Feature Based ◽  
Inspection Schemes

Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.

scholarly journals A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES

2014 ◽  
Vol 33 (1) ◽  
pp. 13 ◽  
Author(s):  
Mehdi Alilou ◽  
Vassili Kovalev ◽  
Eduard Snezhko ◽  
Vahid Taimouri
Keyword(s):  
Early Stage ◽  
Pulmonary Nodules ◽  
Maximum Intensity Projection ◽  
Region Growing ◽  
Ct Images ◽  
Support Vector ◽  
Solitary Pulmonary Nodules ◽  
Computed Tomography Images ◽  
Lung Structure ◽  
Multiple Thresholds

Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx) framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM) is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC) image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9). Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP) volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.

scholarly journals A Feature-Based Analysis for Time-Series Classification of COVID-19 Incidence in Chile: A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 7080
Author(s):  
Christopher Flores ◽  
Carla Taramasco ◽  
Maria Elena Lagos ◽  
Carla Rimassa ◽  
Rosa Figueroa
Keyword(s):  
Short Term Memory ◽  
National Level ◽  
Point Of View ◽  
Support Vector ◽  
Mechanical Ventilators ◽  
Feature Based ◽  
Risk Categories ◽  
Better Than

The 2019 Coronavirus disease (COVID-19) pandemic is a current challenge for the world’s health systems aiming to control this disease. From an epidemiological point of view, the control of the incidence of this disease requires an understanding of the influence of the variables describing a population. This research aims to predict the COVID-19 incidence in three risk categories using two types of machine learning models, together with an analysis of the relative importance of the available features in predicting the COVID-19 incidence in the Chilean urban commune of Concepción. The classification results indicate that the ConvLSTM (Convolutional Long Short-Term Memory) classifier performed better than the SVM (Support Vector Machine), with results between 93% and 96% in terms of accuracy (ACC) and F-measure (F1) metrics. In addition, when considering each one of the regional and national features as well as the communal features (DEATHS and MOBILITY), it was observed that at the regional level the CRITICAL BED OCCUPANCY and PATIENTS IN ICU features positively contributed to the performance of the classifiers, while at the national level the features that most impacted the performance of the SVM and ConvLSTM were those related to the type of hospitalization of patients and the use of mechanical ventilators.

An evolvable self-organizing neuro-fuzzy multilayered classifier with group method data handling and grammar-based bio-inspired supervisors for fault diagnosis of hydraulic systems

2014 ◽  
Vol 7 (1) ◽  
pp. 38-78 ◽  
Author(s):  
Ahmad Mozaffari ◽  
Alireza Fathi ◽  
Saeed Behzadipour
Keyword(s):  
Fault Diagnosis ◽  
Hydraulic System ◽  
Support Vector ◽  
Group Method ◽  
Data Handling ◽  
Fuzzy Classifier ◽  
Content Type ◽  
Neuro Fuzzy ◽  
Group Method Data Handling ◽  
Self Organizing

Purpose – The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits. Design/methodology/approach – In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner. Findings – Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC. Originality/value – The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.

Pulmonary Nodules Detection in CT Scanned Images Based on Optimization Region Growing and MCSVMs

2012 ◽  
Vol 542-543 ◽  
pp. 1306-1310
Author(s):  
Jing Rong Zhao ◽  
Qing Zhu Wang ◽  
Xin Gang Wang ◽  
Jin Zhou
Keyword(s):  
Image Segmentation ◽  
Regional Growth ◽  
Solution Process ◽  
Pulmonary Nodules ◽  
Principal Component ◽  
Region Growing ◽  
Identification Accuracy ◽  
Support Vector ◽  
Seed Point ◽  
Lung Ct

Lung CT images tumor automatic recognition method combined parameter optimization of regional growth with matMCSVMs (Matrix-mode within-class scatter support vector machine) was proposed to reduce the influence of lung CT image segmentation and support vector (SVM) identification in computer aided the tumor detection. Firstly, in view of the main problems existing in the traditional region growing, such that the initial seed point was too sensitive to select and the growth constraint criterion was difficult to determine, 2D PCA (2D principal component analysis) method was put forward. Secondly, a new growth criteria based on watershed range diagram was also designed for image segmentation. And then, aiming at the current problem that it was hard to compute the decision vector iterative operation in the matMCSVMs, a new method with a preprocessing conjugate gradient descent was used to optimize solution process and improve the classifier identification accuracy. The experimental results show that the methods are effective.

scholarly journals Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach

2008 ◽  
Vol 27 (4) ◽  
pp. 467-480 ◽  
Author(s):  
J. Dehmeshki ◽  
H. Amin ◽  
M. Valdivieso ◽  
Xujiong Ye
Keyword(s):  
Pulmonary Nodules ◽  
Region Growing ◽  
Ct Scans ◽  
Thoracic Ct

scholarly journals A Neuro-Fuzzy Approach in the Classification of Students’ Academic Performance

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Quang Hung Do ◽  
Jeng-Fung Chen
Keyword(s):  
Academic Performance ◽  
High Accuracy ◽  
Educational Institutions ◽  
Support Vector ◽  
Fuzzy Approach ◽  
Related Factors ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Admission Decisions ◽  
Input Variables

Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.

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