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Tarquino J, Arabyarmohammadi S, Tejada RE, Madabhushi A, Romero E. Intra-nucleus mosaic pattern (InMop) and whole-cell Haralick combined-descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images. Cytometry A 2023; 103:857-867. [PMID: 37565838 PMCID: PMC10841385 DOI: 10.1002/cyto.a.24785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 07/14/2023] [Accepted: 08/08/2023] [Indexed: 08/12/2023]
Abstract
Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.
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Affiliation(s)
- Jonathan Tarquino
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Sara Arabyarmohammadi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Rafael Enrique Tejada
- Department of internal medicine, Hemato-oncology unit, Medicine Faculty, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Atlanta Veterans Medical Center, Atlanta, GA, USA
| | - Eduardo Romero
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá, Colombia
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Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction. Neural Comput Appl 2023; 35:10473-10496. [PMID: 36747886 PMCID: PMC9894525 DOI: 10.1007/s00521-023-08244-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/06/2023] [Indexed: 02/05/2023]
Abstract
Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction.
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Sowan B, Eshtay M, Dahal K, Qattous H, Zhang L. Hybrid PSO feature selection-based association classification approach for breast cancer detection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07950-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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4
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A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs. SENSORS 2022; 22:s22155566. [PMID: 35898070 PMCID: PMC9332569 DOI: 10.3390/s22155566] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/10/2022] [Accepted: 07/22/2022] [Indexed: 11/17/2022]
Abstract
Medical audio classification for lung abnormality diagnosis is a challenging problem owing to comparatively unstructured audio signals present in the respiratory sound clips. To tackle such challenges, we propose an ensemble model by incorporating diverse deep neural networks with attention mechanisms for undertaking lung abnormality and COVID-19 diagnosis using respiratory, speech, and coughing audio inputs. Specifically, four base deep networks are proposed, which include attention-based Convolutional Recurrent Neural Network (A-CRNN), attention-based bidirectional Long Short-Term Memory (A-BiLSTM), attention-based bidirectional Gated Recurrent Unit (A-BiGRU), as well as Convolutional Neural Network (CNN). A Particle Swarm Optimization (PSO) algorithm is used to optimize the training parameters of each network. An ensemble mechanism is used to integrate the outputs of these base networks by averaging the probability predictions of each class. Evaluated using respiratory ICBHI, Coswara breathing, speech, and cough datasets, as well as a combination of ICBHI and Coswara breathing databases, our ensemble model and base networks achieve ICBHI scores ranging from 0.920 to 0.9766. Most importantly, the empirical results indicate that a positive COVID-19 diagnosis can be distinguished to a high degree from other more common respiratory diseases using audio recordings, based on the combined ICBHI and Coswara breathing datasets.
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El Alaoui Y, Elomri A, Qaraqe M, Padmanabhan R, Yasin Taha R, El Omri H, El Omri A, Aboumarzouk O. A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects. J Med Internet Res 2022; 24:e36490. [PMID: 35819826 PMCID: PMC9328784 DOI: 10.2196/36490] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 05/14/2022] [Accepted: 05/29/2022] [Indexed: 12/23/2022] Open
Abstract
Background Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. Objective This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer. Methods We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. Results Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. Conclusions The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.
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Affiliation(s)
- Yousra El Alaoui
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Adel Elomri
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Marwa Qaraqe
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Regina Padmanabhan
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Ruba Yasin Taha
- National Center for Cancer Care and Research, Hamad Medical Corporation, Doha, Qatar
| | - Halima El Omri
- National Center for Cancer Care and Research, Hamad Medical Corporation, Doha, Qatar
| | - Abdelfatteh El Omri
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, Qatar
| | - Omar Aboumarzouk
- Surgical Research Section, Department of Surgery, Hamad Medical Corporation, Doha, Qatar.,College of Medicine, Qatar University, Doha, Qatar.,College of Medicine, University of Glasgow, Glasgow, United Kingdom
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6
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Rashno A, Shafipour M, Fadaei S. Particle ranking: An Efficient Method for Multi-Objective Particle Swarm Optimization Feature Selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108640] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Applying particle swarm optimization-based decision tree classifier for wart treatment selection. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-021-00348-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractWart is a disease caused by human papillomavirus with common and plantar warts as general forms. Commonly used methods to treat warts are immunotherapy and cryotherapy. The selection of proper treatment is vital to cure warts. This paper establishes a classification and regression tree (CART) model based on particle swarm optimisation to help patients choose between immunotherapy and cryotherapy. The proposed model can accurately predict the response of patients to the two methods. Using an improved particle swarm algorithm (PSO) to optimise the parameters of the model instead of the traditional pruning algorithm, a more concise and more accurate model is obtained. Two experiments are conducted to verify the feasibility of the proposed model. On the hand, five benchmarks are used to verify the performance of the improved PSO algorithm. On the other hand, the experiment on two wart datasets is conducted. Results show that the proposed model is effective. The proposed method classifies better than k-nearest neighbour, C4.5 and logistic regression. It also performs better than the conventional optimisation method for the CART algorithm. Moreover, the decision tree model established in this study is interpretable and understandable. Therefore, the proposed model can help patients and doctors reduce the medical cost and improve the quality of healing operation.
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Wang Z, Zheng X, Li D, Zhang H, Yang Y, Pan H. A VGGNet-like approach for classifying and segmenting coal dust particles with overlapping regions. COMPUT IND 2021. [DOI: 10.1016/j.compind.2021.103506] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Hamid TMTA, Sallehuddin R, Yunos ZM, Ali A. Ensemble Based Filter Feature Selection with Harmonize Particle Swarm Optimization and Support Vector Machine for Optimal Cancer Classification. MACHINE LEARNING WITH APPLICATIONS 2021. [DOI: 10.1016/j.mlwa.2021.100054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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11
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Cancer cells population control in a delayed-model of a leukemic patient using the combination of the eligibility traces algorithm and neural networks. INT J MACH LEARN CYB 2021. [DOI: 10.1007/s13042-021-01287-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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BAI GMERCY, VENKADESH P. TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES. J MECH MED BIOL 2021. [DOI: 10.1142/s021951942150041x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating the Taylor series and MBO, respectively. The sparking process is designed to perform the automatic segmentation of blood smear images by estimating optimal threshold values. By extracting the features, such as texture features, statistical, and grid-based features from the segmented smear image, the performance of classification is increased with less training time. The kernel function of SVM is enabled to perform the leukemia classification such that the proposed Taylor–MBO algorithm accomplishes the training process of SVM. However, the proposed Taylor–MBO-based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity, with 94.5751, 95.526, and 94.570%, respectively.
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Affiliation(s)
- G. MERCY BAI
- Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India
| | - P. VENKADESH
- Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India
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13
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Intelligent human action recognition using an ensemble model of evolving deep networks with swarm-based optimization. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106918] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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14
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Xie H, Zhang L, Lim CP, Yu Y, Liu H. Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models. SENSORS 2021; 21:s21051816. [PMID: 33807806 PMCID: PMC7961412 DOI: 10.3390/s21051816] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 11/16/2022]
Abstract
In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.
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Affiliation(s)
- Hailun Xie
- Computational Intelligence Research Group, Department of Computer and Information Sciences, Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, UK;
| | - Li Zhang
- Computational Intelligence Research Group, Department of Computer and Information Sciences, Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, UK;
- Correspondence:
| | - Chee Peng Lim
- Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC 3216, Australia;
| | - Yonghong Yu
- College of Tongda, Nanjing University of Posts and Telecommunications, Nanjing 210049, China;
| | - Han Liu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;
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15
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Irshad S, Yin X, Zhang Y. A new approach for retinal vessel differentiation using binary particle swarm optimization. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2021. [DOI: 10.1080/21681163.2020.1870001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Samra Irshad
- School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Australia
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China
| | - Yanchun Zhang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
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Sun J, Wang L, Liu Q, Tárnok A, Su X. Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification. BIOMEDICAL OPTICS EXPRESS 2020; 11:6674-6686. [PMID: 33282516 PMCID: PMC7687967 DOI: 10.1364/boe.405557] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 10/14/2020] [Accepted: 10/14/2020] [Indexed: 05/27/2023]
Abstract
The subtyping of Acute lymphocytic leukemia (ALL) is important for proper treatment strategies and prognosis. Conventional methods for manual blood and bone marrow testing are time-consuming and labor-intensive, while recent flow cytometric immunophenotyping has the limitations such as high cost. Here we develop the deep learning-based light scattering imaging flow cytometry for label-free classification of ALL. The single ALL cells confined in three dimensional (3D) hydrodynamically focused stream are excited by light sheet. Our label-free microfluidic cytometry obtains big-data two dimensional (2D) light scattering patterns from single ALL cells of B/T subtypes. A deep learning framework named Inception V3-SIFT (Scale invariant feature transform)-Scattering Net (ISSC-Net) is developed, which can perform high-precision classification of T-ALL and B-ALL cell line cells with an accuracy of 0.993 ± 0.003. Our deep learning-based 2D light scattering flow cytometry is promising for automatic and accurate subtyping of un-stained ALL.
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Affiliation(s)
- Jing Sun
- School of Microelectronics, Shandong University, Jinan, China
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Lan Wang
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qiao Liu
- Key Laboratory of Experimental Teratology (Ministry of Education); Department of Molecular Medicine and Genetics, School of Basic Medicine Sciences, Shandong University, Jinan, China
| | - Attila Tárnok
- Department of Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany
| | - Xuantao Su
- School of Microelectronics, Shandong University, Jinan, China
- Advanced Medical Research Institute, Shandong University, Jinan, China
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Yang C, Liu T, Yi W, Chen X, Niu B. Identifying expertise through semantic modeling: A modified BBPSO algorithm for the reviewer assignment problem. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106483] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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18
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An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli. Processes (Basel) 2020. [DOI: 10.3390/pr8080963] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.
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19
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Praveena S, Singh SP. Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia. BIOMED ENG-BIOMED TE 2020; 65:/j/bmte.ahead-of-print/bmt-2018-0213/bmt-2018-0213.xml. [PMID: 32706747 DOI: 10.1515/bmt-2018-0213] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Accepted: 05/13/2020] [Indexed: 02/28/2024]
Abstract
Leukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.
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Affiliation(s)
- Segu Praveena
- Department of Electronics and Communication Engineering, Mahatma Gandhi Institute of Technology, Kokapet, Hyderabad, Telangana, India
| | - Sohan Pal Singh
- Department of Electronics and Communication Engineering, Mahatma Gandhi Institute of Technology, Kokapet, Hyderabad, Telangana, India
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20
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Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106328] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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21
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Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm. ELECTRONICS 2020. [DOI: 10.3390/electronics9010188] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Segmenting an image of a nucleus is one of the most essential tasks in a leukemia diagnostic system. Accurate and rapid segmentation methods help the physicians identify the diseases and provide better treatment at the appropriate time. Recently, hybrid clustering algorithms have started being widely used for image segmentation in medical image processing. In this article, a novel hybrid histogram-based soft covering rough k-means clustering (HSCRKM) algorithm for leukemia nucleus image segmentation is discussed. This algorithm combines the strengths of a soft covering rough set and rough k-means clustering. The histogram method was utilized to identify the number of clusters to avoid random initialization. Different types of features such as gray level co-occurrence matrix (GLCM), color, and shape-based features were extracted from the segmented image of the nucleus. Machine learning prediction algorithms were applied to classify the cancerous and non-cancerous cells. The proposed strategy is compared with an existing clustering algorithm, and the efficiency is evaluated based on the prediction metrics. The experimental results show that the HSCRKM method efficiently segments the nucleus, and it is also inferred that logistic regression and neural network perform better than other prediction algorithms.
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22
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Tan TY, Zhang L, Lim CP. Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.06.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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23
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Jha KK, Dutta HS. Nucleus and cytoplasm-based segmentation and actor-critic neural network for acute lymphocytic leukaemia detection in single cell blood smear images. Med Biol Eng Comput 2019; 58:171-186. [PMID: 31811554 DOI: 10.1007/s11517-019-02071-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Accepted: 11/05/2019] [Indexed: 01/19/2023]
Abstract
Acute lymphoblastic leukaemia (ALL), which is due to the malfunctioning in the bone marrow, is common among people all over the world. The haematologist suffers a lot to discriminate the presence of leukaemia in the patients using the blood smears. To overcome the inaccuracy and reliability issues, this paper proposes an automatic method of leukaemia detection, named chronological Sine Cosine Algorithm-based actor-critic neural network (Chrono-SCA-ACNN). Initially, the blood smear images are segmented using the proposed entropy-based hybrid model, from which the image-level features and statistical features are extracted from the segments. Then, the selected features are applied to the proposed classifier, which detects the leukaemia. In the proposed Chrono-SCA-ACNN, the optimal weights are selected by the proposed Chrono-SCA, which is the integration of the chronological concept in the SCA. Finally, the experimentation is performed using the ALL-IDB2 database, and the effectiveness of the proposed method over the existing methods is evaluated. From the analysis, the accuracy of the proposed method is found to be 0.99, which proves that it outperforms the existing classification methodologies. Graphical abstract Block diagram of proposed Leukaemia detection. The main aim of the paper is to segment and classify the WBCs for ALL detection in single cell blood smear images. Initially, the blood smear image is subjected to pre-processing in order to enhance the quality of the input image so as to make it effective for the further processes associated with Leukaemia detection. The pre-processed image is applied to the segmentation process that segments the cytoplasm and nucleus using the Entropy-based hybrid model. The entropy-based hybrid model is developed using the FCM and active contour to segment the cytoplasm and nucleus that is fused using the entropy. The segments are subjected to the feature extraction that extracts the statistical features and the color histogram-based features from the segments. The features are presented to the Actor-Critic Neural Network and the weights of the Neural Network (NN) are optimally tuned using the proposed Chrono-SCA. The block diagram of the proposed method of leukaemia detection is depicted in Fig. 1.
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Affiliation(s)
- Krishna Kumar Jha
- Calcutta Institute of Technology, Banitabla, Uluberia, Howrah - 711316, India.
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Hegde RB, Prasad K, Hebbar H, Singh BMK, Sandhya I. Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images. J Digit Imaging 2019; 33:361-374. [PMID: 31728805 DOI: 10.1007/s10278-019-00288-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Peripheral blood smear analysis plays a vital role in diagnosing many diseases including cancer. Leukemia is a type of cancer which begins in bone marrow and results in increased number of white blood cells in peripheral blood. Unusual variations in appearance of white blood cells indicate leukemia. In this paper, an automated method for detection of leukemia using image processing approach is proposed. In the present study, 1159 images of different brightness levels and color shades were acquired from Leishman stained peripheral blood smears. SVM classifier was used for classification of white blood cells into normal and abnormal, and also for detection of leukemic WBCs from the abnormal class. Classification of the normal white blood cells into five sub-types was performed using NN classifier. Overall classification accuracy of 98.8% was obtained using the combination of NN and SVM.
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Affiliation(s)
- Roopa B Hegde
- Manipal School of Information Sciences, MAHE, Manipal, 576104, India. .,Department of ECE, NMAM Institute of Technology (Visvesvaraya Technological Univerity, Belagavi), Nitte, Karnataka, 574110, India.
| | - Keerthana Prasad
- Manipal School of Information Sciences, MAHE, Manipal, 576104, India
| | | | - Brij Mohan Kumar Singh
- Department of Immunohematology and Blood Transfusion KMC, MAHE, Manipal, Karnataka, 576104, India
| | - I Sandhya
- Department of Pathology, A J Institute of Medical Sciences and Research Center, Kuntikana, Mangalore, 575004, India
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Xie H, Zhang L, Lim CP, Yu Y, Liu C, Liu H, Walters J. Improving K-means clustering with enhanced Firefly Algorithms. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105763] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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26
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Tan TY, Zhang L, Lim CP. Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105725] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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27
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Halim NHA, Mashor MY, Hassan R. Classification of Acute Leukemia Based on Multilayer Perceptron. JOURNAL OF PHYSICS: CONFERENCE SERIES 2019; 1372:012044. [DOI: 10.1088/1742-6596/1372/1/012044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Abstract
In general, various artificial neural network have been applied in many areas such as modelling, pattern recognition, signal processing, diagnostic and prognostic. In this paper, artificial neural network are used to detect and classify the white blood cell (WBC) inside the acute leukemia blood samples. There are 25 features have been extracted from segmented WBC, which consist of shape, color and texture based features. Then, it have been fed up as the neural network inputs for the classification process in order to classify the segmented regions into two classes either B or T. The training algorithm for MLP network is Levenberg-Marquardt (LM). The MLP network achieves the highest testing accuracy of 96.99% for 4 hidden nodes at state of 5 by using the overall 25 input features. Thus, MLP network trained by using LM algorithm is suitable for acute leukemia cells detection in blood sample.
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28
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Jha KK, Dutta HS. Mutual Information based hybrid model and deep learning for Acute Lymphocytic Leukemia detection in single cell blood smear images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 179:104987. [PMID: 31443862 DOI: 10.1016/j.cmpb.2019.104987] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 06/20/2019] [Accepted: 07/14/2019] [Indexed: 05/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Due to the development in digital microscopic imaging, image processing and classification has become an interesting area for diagnostic research. Various techniques are available in the literature for the detection of Acute Lymphocytic Leukemia from the single cell blood smear images. The purpose of this work is to develop an effective method for leukemia detection. METHODS This work has developed deep learning based leukemia detection module from the blood smear images. Here, the detection scheme carries out pre-processing, segmentation, feature extraction and classification. The segmentation is done by the proposed Mutual Information (MI) based hybrid model, which combines the segmentation results of the active contour model and fuzzy C means algorithm. Then, from the segmented images, the statistical and the Local Directional Pattern (LDP) features are extracted and provided to the proposed Chronological Sine Cosine Algorithm (SCA) based Deep CNN classifier for the classification. RESULTS For the experimentation, the blood smear images are considered from the AA-IDB2 database and evaluated based on metrics, such as True Positive Rate (TPR), True Negative Rate (TNR), and accuracy. Simulation results reveal that the proposed Chronological SCA based Deep CNN classifier has the accuracy of 98.7%. CONCLUSIONS The performance of the proposed Chronological SCA-based Deep CNN classifier is compared with the state-of-the-art methods. The analysis shows that the proposed classifier has comparatively improved performance and determines the leukemia from the blood smear images.
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Multiclass Benchmarking Framework for Automated Acute Leukaemia Detection and Classification Based on BWM and Group-VIKOR. J Med Syst 2019; 43:212. [PMID: 31154550 DOI: 10.1007/s10916-019-1338-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 05/13/2019] [Indexed: 10/26/2022]
Abstract
This paper aims to assist the administration departments of medical organisations in making the right decision on selecting a suitable multiclass classification model for acute leukaemia. In this paper, we proposed a framework that will aid these departments in evaluating, benchmarking and ranking available multiclass classification models for the selection of the best one. Medical organisations have continuously faced evaluation and benchmarking challenges in such endeavour, especially when no single model is superior. Moreover, the improper selection of multiclass classification for acute leukaemia model may be costly for medical organisations. For example, when a patient dies, one such organisation will be legally or financially sued for incidents in which the model fails to fulfil its desired outcome. With regard to evaluation and benchmarking, multiclass classification models are challenging processes due to multiple evaluation and conflicting criteria. This study structured a decision matrix (DM) based on the crossover of 2 groups of multi-evaluation criteria and 22 multiclass classification models. The matrix was then evaluated with datasets comprising 72 samples of acute leukaemia, which include 5327 gens. Subsequently, multi-criteria decision-making (MCDM) techniques are used in the benchmarking and ranking of multiclass classification models. The MCDM used techniques that include the integrated BWM and VIKOR. BWM has been applied for the weight calculations of evaluation criteria, whereas VIKOR has been used to benchmark and rank classification models. VIKOR has also been employed in two decision-making contexts: individual and group decision making and internal and external group aggregation. Results showed the following: (1) the integration of BWM and VIKOR is effective at solving the benchmarking/selection problems of multiclass classification models. (2) The ranks of classification models obtained from internal and external VIKOR group decision making were almost the same, and the best multiclass classification model based on the two was 'Bayes. Naive Byes Updateable' and the worst one was 'Trees.LMT'. (3) Among the scores of groups in the objective validation, significant differences were identified, which indicated that the ranking results of internal and external VIKOR group decision making were valid.
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30
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An improved particle swarm optimization (PSO): method to enhance modeling of airborne particulate matter (PM10). EVOLVING SYSTEMS 2019. [DOI: 10.1007/s12530-019-09263-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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31
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Tan TY, Zhang L, Neoh SC, Lim CP. Intelligent skin cancer detection using enhanced particle swarm optimization. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.05.042] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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32
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Alsalem MA, Zaidan AA, Zaidan BB, Hashim M, Albahri OS, Albahri AS, Hadi A, Mohammed KI. Systematic Review of an Automated Multiclass Detection and Classification System for Acute Leukaemia in Terms of Evaluation and Benchmarking, Open Challenges, Issues and Methodological Aspects. J Med Syst 2018; 42:204. [PMID: 30232632 DOI: 10.1007/s10916-018-1064-9] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 09/06/2018] [Indexed: 10/28/2022]
Abstract
This study aims to systematically review prior research on the evaluation and benchmarking of automated acute leukaemia classification tasks. The review depends on three reliable search engines: ScienceDirect, Web of Science and IEEE Xplore. A research taxonomy developed for the review considers a wide perspective for automated detection and classification of acute leukaemia research and reflects the usage trends in the evaluation criteria in this field. The developed taxonomy consists of three main research directions in this domain. The taxonomy involves two phases. The first phase includes all three research directions. The second one demonstrates all the criteria used for evaluating acute leukaemia classification. The final set of studies includes 83 investigations, most of which focused on enhancing the accuracy and performance of detection and classification through proposed methods or systems. Few efforts were made to undertake the evaluation issues. According to the final set of articles, three groups of articles represented the main research directions in this domain: 56 articles highlighted the proposed methods, 22 articles involved proposals for system development and 5 papers centred on evaluation and comparison. The other taxonomy side included 16 main and sub-evaluation and benchmarking criteria. This review highlights three serious issues in the evaluation and benchmarking of multiclass classification of acute leukaemia, namely, conflicting criteria, evaluation criteria and criteria importance. It also determines the weakness of benchmarking tools. To solve these issues, multicriteria decision-making (MCDM) analysis techniques were proposed as effective recommended solutions in the methodological aspect. This methodological aspect involves a proposed decision support system based on MCDM for evaluation and benchmarking to select suitable multiclass classification models for acute leukaemia. The said support system is examined and has three sequential phases. Phase One presents the identification procedure and process for establishing a decision matrix based on a crossover of evaluation criteria and acute leukaemia multiclass classification models. Phase Two describes the decision matrix development for the selection of acute leukaemia classification models based on the integrated Best and worst method (BWM) and VIKOR. Phase Three entails the validation of the proposed system.
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Affiliation(s)
- M A Alsalem
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - A A Zaidan
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia.
| | - B B Zaidan
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - M Hashim
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - O S Albahri
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - A S Albahri
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - Ali Hadi
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
| | - K I Mohammed
- Department of Computing, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia
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Pandit D, Zhang L, Chattopadhyay S, Lim CP, Liu C. A scattering and repulsive swarm intelligence algorithm for solving global optimization problems. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.05.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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Alsalem MA, Zaidan AA, Zaidan BB, Hashim M, Madhloom HT, Azeez ND, Alsyisuf S. A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:93-112. [PMID: 29544792 DOI: 10.1016/j.cmpb.2018.02.005] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Revised: 01/19/2018] [Accepted: 02/02/2018] [Indexed: 06/08/2023]
Abstract
CONTEXT Acute leukaemia diagnosis is a field requiring automated solutions, tools and methods and the ability to facilitate early detection and even prediction. Many studies have focused on the automatic detection and classification of acute leukaemia and their subtypes to promote enable highly accurate diagnosis. OBJECTIVE This study aimed to review and analyse literature related to the detection and classification of acute leukaemia. The factors that were considered to improve understanding on the field's various contextual aspects in published studies and characteristics were motivation, open challenges that confronted researchers and recommendations presented to researchers to enhance this vital research area. METHODS We systematically searched all articles about the classification and detection of acute leukaemia, as well as their evaluation and benchmarking, in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 2007 to 2017. These indices were considered to be sufficiently extensive to encompass our field of literature. RESULTS Based on our inclusion and exclusion criteria, 89 articles were selected. Most studies (58/89) focused on the methods or algorithms of acute leukaemia classification, a number of papers (22/89) covered the developed systems for the detection or diagnosis of acute leukaemia and few papers (5/89) presented evaluation and comparative studies. The smallest portion (4/89) of articles comprised reviews and surveys. DISCUSSION Acute leukaemia diagnosis, which is a field requiring automated solutions, tools and methods, entails the ability to facilitate early detection or even prediction. Many studies have been performed on the automatic detection and classification of acute leukaemia and their subtypes to promote accurate diagnosis. CONCLUSIONS Research areas on medical-image classification vary, but they are all equally vital. We expect this systematic review to help emphasise current research opportunities and thus extend and create additional research fields.
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Affiliation(s)
- M A Alsalem
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - A A Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia.
| | - B B Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - M Hashim
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - H T Madhloom
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - N D Azeez
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Malaysia
| | - S Alsyisuf
- Faculty of on information Science and Engineering, Management and Science university, Shah Alam, Malaysia
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