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Fadja AN, Fraccaroli M, Bizzarri A, Mazzuchelli G, Lamma E. Neural-Symbolic Ensemble Learning for early-stage prediction of critical state of Covid-19 patients. Med Biol Eng Comput 2022; 60:3461-3474. [PMID: 36201136 PMCID: PMC9540054 DOI: 10.1007/s11517-022-02674-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 09/17/2022] [Indexed: 11/11/2022]
Abstract
Recently, Artificial Intelligence (AI) and Machine Learning (ML) have been successfully applied to many domains of interest including medical diagnosis. Due to the availability of a large quantity of data, it is possible to build reliable AI systems that assist humans in making decisions. The recent Covid-19 pandemic quickly spread over the world causing serious health problems and severe economic and social damage. Computer scientists are actively working together with doctors on different ML models to diagnose Covid-19 patients using Computed Tomography (CT) scans and clinical data. In this work, we propose a neural-symbolic system that predicts if a Covid-19 patient arriving at the hospital will end in a critical condition. The proposed system relies on Deep 3D Convolutional Neural Networks (3D-CNNs) for analyzing lung CT scans of Covid-19 patients, Decision Trees (DTs) for predicting if a Covid-19 patient will eventually pass away by analyzing its clinical data, and a neural system that integrates the previous ones using Hierarchical Probabilistic Logic Programs (HPLPs). Predicting if a Covid-19 patient will end in a critical condition is useful for managing the limited number of intensive care at the hospital. Moreover, knowing early that a Covid-19 patient could end in serious conditions allows doctors to gain early knowledge on patients and provide special treatment to those predicted to finish in critical conditions. The proposed system, entitled Neural HPLP, obtains good performance in terms of area under the receiver operating characteristic and precision curves with values of about 0.96 for both metrics. Therefore, with Neural HPLP, it is possible not only to efficiently predict if Covid-19 patients will end in severe conditions but also possible to provide an explanation of the prediction. This makes Neural HPLP explainable, interpretable, and reliable. Graphical abstract Representation of Neural HPLP. From top to bottom, the two different types of data collected from the same patient and used in this project are represented. This data feeds the two different machine learning systems and the integration of the two systems using Hierarchical Probabilistic Logic Program.
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Affiliation(s)
- Arnaud Nguembang Fadja
- Department of Mathematics and Computer Science, University of Ferrara, Via Nicolò Machiavelli 30, Ferrara, 44121 Italy
| | - Michele Fraccaroli
- DE - Department of Engineering, University of Ferrara, Via Saragat 1, Ferrara, 44122 Italy
| | - Alice Bizzarri
- DE - Department of Engineering, University of Ferrara, Via Saragat 1, Ferrara, 44122 Italy
| | - Giulia Mazzuchelli
- DE - Department of Engineering, University of Ferrara, Via Saragat 1, Ferrara, 44122 Italy
| | - Evelina Lamma
- DE - Department of Engineering, University of Ferrara, Via Saragat 1, Ferrara, 44122 Italy
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Saeed M, Ahsan M, Ur Rahman A, Saeed MH, Mehmood A. An application of neutrosophic hypersoft mapping to diagnose brain tumor and propose appropriate treatment. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-210482] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Brain tumors are one of the leading causes of death around the globe. More than 10 million people fall prey to it every year. This paper aims to characterize the discussions related to the diagnosis of tumors with their related problems. After examining the side effects of tumors, it encases similar indications, and it is hard to distinguish the precise type of tumors with their seriousness. Since in practical assessment, the indeterminacy and falsity parts are frequently dismissed, and because of this issue, it is hard to notice the precision in the patient’s progress history and cannot foresee the period of treatment. The Neutrosophic Hypersoft set (NHS) and the NHS mapping with its inverse mapping has been design to overcome this issue since it can deal with the parametric values of such disease in more detail considering the sub-parametric values; and their order and arrangement. These ideas are capable and essential to analyze the issue properly by interfacing it with scientific modeling. This investigation builds up a connection between symptoms and medicines, which diminishes the difficulty of the narrative. A table depending on a fuzzy interval between [0, 1] for the sorts of tumors is constructed. The calculation depends on NHS mapping to adequately recognize the disease and choose the best medication for each patient’s relating sickness. Finally, the generalized NHS mapping is presented, which will encourage a specialist to extricate the patient’s progress history and to foresee the time of treatment till the infection is relieved.
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Affiliation(s)
- Muhammad Saeed
- Department of Mathematics, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Muhammad Ahsan
- Department of Mathematics, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Atiqe Ur Rahman
- Department of Mathematics, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Muhammad Haris Saeed
- Department of Chemistry, University of Management and Technology, Lahore, Punjab, Pakistan
| | - Asad Mehmood
- Department of Mathematics, University of Management and Technology, Lahore, Punjab, Pakistan
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Miao J, Zhou X, Huang TZ. Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106200] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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MF2C3: Multi-Feature Fuzzy Clustering to Enhance Cell Colony Detection in Automated Clonogenic Assay Evaluation. Symmetry (Basel) 2020. [DOI: 10.3390/sym12050773] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
A clonogenic assay is a biological technique for calculating the Surviving Fraction (SF) that quantifies the anti-proliferative effect of treatments on cell cultures: this evaluation is often performed via manual counting of cell colony-forming units. Unfortunately, this procedure is error-prone and strongly affected by operator dependence. Besides, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or administered cytotoxic agent. Relying upon the direct proportional relationship between the Area Covered by Colony (ACC) and the colony count and size, along with the growth rate, we propose MF2C3, a novel computational method leveraging spatial Fuzzy C-Means clustering on multiple local features (i.e., entropy and standard deviation extracted from the input color images acquired by a general-purpose flat-bed scanner) for ACC-based SF quantification, by considering only the covering percentage. To evaluate the accuracy of the proposed fully automatic approach, we compared the SFs obtained by MF2C3 against the conventional counting procedure on four different cell lines. The achieved results revealed a high correlation with the ground-truth measurements based on colony counting, by outperforming our previously validated method using local thresholding on L*u*v* color well images. In conclusion, the proposed multi-feature approach, which inherently leverages the concept of symmetry in the pixel local distributions, might be reliably used in biological studies.
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Rundo L, Beer L, Ursprung S, Martin-Gonzalez P, Markowetz F, Brenton JD, Crispin-Ortuzar M, Sala E, Woitek R. Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering. Comput Biol Med 2020; 120:103751. [PMID: 32421652 PMCID: PMC7248575 DOI: 10.1016/j.compbiomed.2020.103751] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 04/03/2020] [Accepted: 04/05/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Cancer typically exhibits genotypic and phenotypic heterogeneity, which can have prognostic significance and influence therapy response. Computed Tomography (CT)-based radiomic approaches calculate quantitative features of tumour heterogeneity at a mesoscopic level, regardless of macroscopic areas of hypo-dense (i.e., cystic/necrotic), hyper-dense (i.e., calcified), or intermediately dense (i.e., soft tissue) portions. METHOD With the goal of achieving the automated sub-segmentation of these three tissue types, we present here a two-stage computational framework based on unsupervised Fuzzy C-Means Clustering (FCM) techniques. No existing approach has specifically addressed this task so far. Our tissue-specific image sub-segmentation was tested on ovarian cancer (pelvic/ovarian and omental disease) and renal cell carcinoma CT datasets using both overlap-based and distance-based metrics for evaluation. RESULTS On all tested sub-segmentation tasks, our two-stage segmentation approach outperformed conventional segmentation techniques: fixed multi-thresholding, the Otsu method, and automatic cluster number selection heuristics for the K-means clustering algorithm. In addition, experiments showed that the integration of the spatial information into the FCM algorithm generally achieves more accurate segmentation results, whilst the kernelised FCM versions are not beneficial. The best spatial FCM configuration achieved average Dice similarity coefficient values starting from 81.94±4.76 and 83.43±3.81 for hyper-dense and hypo-dense components, respectively, for the investigated sub-segmentation tasks. CONCLUSIONS The proposed intelligent framework could be readily integrated into clinical research environments and provides robust tools for future radiomic biomarker validation.
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Affiliation(s)
- Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Lucian Beer
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna 1090, Austria.
| | - Stephan Ursprung
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Paula Martin-Gonzalez
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Florian Markowetz
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
| | - James D Brenton
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Mireia Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK.
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna 1090, Austria.
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Schwämmle V, Jensen ON. VSClust: feature-based variance-sensitive clustering of omics data. Bioinformatics 2019; 34:2965-2972. [PMID: 29635359 DOI: 10.1093/bioinformatics/bty224] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 04/06/2018] [Indexed: 12/25/2022] Open
Abstract
Motivation Data clustering is indispensable for identifying biologically relevant molecular features in large-scale omics experiments with thousands of measurements at multiple conditions. Optimal clustering results yield groups of functionally related features that may include genes, proteins and metabolites in biological processes and molecular networks. Omics experiments typically include replicated measurements of each feature within a given condition to statistically assess feature-specific variation. Current clustering approaches ignore this variation by averaging, which often leads to incorrect cluster assignments. Results We present VSClust that accounts for feature-specific variance. Based on an algorithm derived from fuzzy clustering, VSClust unifies statistical testing with pattern recognition to cluster the data into feature groups that more accurately reflect the underlying molecular and functional behavior. We apply VSClust to artificial and experimental datasets comprising hundreds to >80 000 features across 6-20 different conditions including genomics, transcriptomics, proteomics and metabolomics experiments. VSClust avoids arbitrary averaging methods, outperforms standard fuzzy c-means clustering and simplifies the data analysis workflow in large-scale omics studies. Availability and implementation Download VSClust at https://bitbucket.org/veitveit/vsclust or access it through computproteomics.bmb.sdu.dk/Apps/VSClust. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark.,VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense M, Denmark
| | - Ole N Jensen
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark.,VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense M, Denmark
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Quasi-cluster centers clustering algorithm based on potential entropy and t-distributed stochastic neighbor embedding. Soft comput 2018. [DOI: 10.1007/s00500-018-3221-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach. Soft comput 2017. [DOI: 10.1007/s00500-017-2899-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Zhang J, Yin Z, Yang S, Wang R. Operator functional state estimation based on EEG-data-driven fuzzy model. Cogn Neurodyn 2016; 10:375-83. [PMID: 27668017 DOI: 10.1007/s11571-016-9389-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Revised: 01/10/2016] [Accepted: 05/09/2016] [Indexed: 11/26/2022] Open
Abstract
This paper proposed a max-min-entropy-based fuzzy partition method for fuzzy model based estimation of human operator functional state (OFS). The optimal number of fuzzy partitions for each I/O variable of fuzzy model is determined by using the entropy criterion. The fuzzy models were constructed by using Wang-Mendel method. The OFS estimation results showed the practical usefulness of the proposed fuzzy modeling approach.
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Affiliation(s)
- Jianhua Zhang
- Department of Automation, East China University of Science and Technology, Shanghai, 200237 People's Republic of China
| | - Zhong Yin
- Engineering Research Center of Optical Instrument and System, Ministry of Education, University of Shanghai for Science and Technology, Shanghai, 200093 People's Republic of China
| | - Shaozeng Yang
- Department of Automation, East China University of Science and Technology, Shanghai, 200237 People's Republic of China
| | - Rubin Wang
- Institute of Cognitive Neurodynamics, East China University of Science and Technology, Shanghai, 200237 People's Republic of China
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Hemanth DJ, Anitha J, Balas VE. Fast and accurate fuzzy C-means algorithm for MR brain image segmentation. INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2016. [DOI: 10.1002/ima.22176] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
| | - J. Anitha
- Department of ECE; Karunya University; Coimbatore India
| | - Valentina Emilia Balas
- Department of Automation and Applied Informatics; Aurel Vlaicu University of Arad; Romania
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11
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Zareei A, Karimi A. Liver segmentation with new supervised method to create initial curve for active contour. Comput Biol Med 2016; 75:139-50. [DOI: 10.1016/j.compbiomed.2016.05.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Revised: 05/14/2016] [Accepted: 05/17/2016] [Indexed: 01/15/2023]
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14
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XU SHAOPING, HU LINGYAN, LI CHUNQUAN, YANG XIAOHUI, LIU XIAOPINGP. AN UNSUPERVISED COLOR-TEXTURE SEGMENTATION USING TWO-STAGE FUZZY c-MEANS ALGORITHM. INT J PATTERN RECOGN 2014. [DOI: 10.1142/s0218001414550027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Unsupervised image segmentation is a fundamental but challenging problem in computer vision. In this paper, we propose a novel unsupervised segmentation algorithm, which could find diverse applications in pattern recognition, particularly in computer vision. The algorithm, named Two-stage Fuzzy c-means Hybrid Approach (TFHA), adaptively clusters image pixels according to their multichannel Gabor responses taken at multiple scales and orientations. In the first stage, the fuzzy c-means (FCM) algorithm is applied for intelligent estimation of centroid number and initialization of cluster centroids, which endows the novel segmentation algorithm with adaptivity. To improve the efficiency of the algorithm, we utilize the Gray Level Co-occurrence Matrix (GLCM) feature extracted at the hyperpixel level instead of the pixel level to estimate centroid number and hyperpixel-cluster memberships, which are used as initialization parameters of the following main clustering stage to reduce the computational cost while keeping the segmentation performance in terms of accuracy close to original one. Then, in the second stage, the FCM algorithm is utilized again at the pixel level to improve the compactness of the clusters forming final homogeneous regions. To examine the performance of the proposed algorithm, extensive experiments were conducted and experimental results show that the proposed algorithm has a very effective segmentation results and computational behavior, decreases the execution time and increases the quality of segmentation results, compared with the state-of-the-art segmentation methods recently proposed in the literature.
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Affiliation(s)
- SHAOPING XU
- School of Information Engineering, Nanchang University, Nanchang 330031, P. R. China
| | - LINGYAN HU
- School of Information Engineering, Nanchang University, Nanchang 330031, P. R. China
| | - CHUNQUAN LI
- School of Information Engineering, Nanchang University, Nanchang 330031, P. R. China
| | - XIAOHUI YANG
- School of Information Engineering, Nanchang University, Nanchang 330031, P. R. China
| | - XIAOPING P. LIU
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada K1S 5B6, Canada
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Jurio A, Bustince H, Pagola M, Couto P, Pedrycz W. New measures of homogeneity for image processing: an application to fingerprint segmentation. Soft comput 2013. [DOI: 10.1007/s00500-013-1126-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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