Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning

Farahani, Farzad V.; Ahmadi, Abbas; Zarandi, M. H. Fazel

doi:10.1016/j.matcom.2018.02.001

Cited by 59 publications

(28 citation statements)

References 49 publications

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“…This algorithm performs optimization by updating memberships and cluster centers until convergence (Lee et al, 2012; Lahijanian et al, 2016). It's worth noting that, given the non-Euclidean nature of MRI data, the use of Euclidean distance in FCM-based algorithms may lead to an invalid result (Farahani et al, 2015, 2018). van den Heuvel and Hulshoff Pol (2010) compared the results of clustering algorithms to those of decomposition-based methods and reported a high level of overlap.…”

Section: Theoretical Background: Connectivity Patterns Using Fmrimentioning

confidence: 99%

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

2019

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Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

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Section: Theoretical Background: Connectivity Patterns Using Fmrimentioning

confidence: 99%

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

2019

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“…After that, for each one, authors computed the orientation of the straight line which links it to its spatially Nearest Neighbors CC. Lastly, a histogram of all these orientations is calculated, in where the peak value refers to the inclination angle [9,10].…”

Section: Hashizume Et Al Originally Proposed a Descendant Technique mentioning

confidence: 99%

“…(1) Projection profile analysis based methods (2) Nearest-neighbor (NN) clustering based methods (3) Hough transform based methods (4) Cross-correlations based methods (5) Morphological transform based methods (6) Analysis of the background of documents images based methods (7) Statistical mixture model based methods (8) Principal component analysis based methods (9) Radon transform based methods (10) Fourier transform based methods.…”

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confidence: 99%

Using skeleton and Hough transform variant to correct skew in historical documents

Boudraa

Hidouci²,

Michelucci

2020

Mathematics and Computers in Simulation

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As a main part of several document analysis systems, Skew estimation represents one of the major research defies, particularly in case of historical documents exploration. In this paper, we propose an original skew angle detection and correction technique. Morphological Skeleton is introduced to considerably diminish the amount of data by eliminating the redundant pixels and preserving only the central curves of the image components. Next, the proposed method uses Progressive Probabilistic Hough Transform (PPHT) to find image lines. At the end, a specific procedure is applied in order to measure the global skew angle of the document image from these identified lines. Experimental results demonstrate the accuracy and the effectiveness of our approach on skew angle detection upon three popular datasets covering many types of documents of diverse linguistic writings (Chinese, Greek and English) and different styles (horizontal or vertical orientations, including figures and tables, multi-columns page layouts).

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“…To extract significant statistical and morphological features from pulmonary nodules in computed tomography images of the lung and to determine whether the nodule is cancerous or healthy. Farahani et al 49 employed an ensemble of three classifiers comprising MLP, kNN, and SVM. ANNs are much used in cancer applications.…”

Section: Machine Learning Classification Approachesmentioning

confidence: 99%

Heuristic neural network approach in histological sections detection of hydatidiform mole

et al. 2019

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A heuristic-based, multineural network (MNN) image analysis as a solution to the problematical diagnosis of hydatidiform mole (HM) is presented. HM presents as tumors in placental cell structures, many of which exhibit premalignant phenotypes (choriocarcinoma and other conditions). HM is commonly found in women under age 17 or over 35 and can be partial HM or complete HM. Appropriate treatment is determined by correct categorization into PHM or CHM, a difficult task even for expert pathologists. Image analysis combined with pattern recognition techniques has been applied to the problem, based on 15 or 17 image features. The use of limited data for training and validation set was optimized using a k-fold validation technique allowing performance measurement of different MNN configurations. The MNN technique performed better than human experts at the categorization for both the 15-and 17-feature data, promising greater diagnostic consistency, and further improvements with the availability of larger datasets.

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Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning

Cited by 59 publications

References 49 publications

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Using skeleton and Hough transform variant to correct skew in historical documents

Heuristic neural network approach in histological sections detection of hydatidiform mole

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