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Bojsen JA, Elhakim MT, Graumann O, Gaist D, Nielsen M, Harbo FSG, Krag CH, Sagar MV, Kruuse C, Boesen MP, Rasmussen BSB. Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis. Insights Imaging 2024; 15:160. [PMID: 38913106 PMCID: PMC11196541 DOI: 10.1186/s13244-024-01723-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 05/23/2024] [Indexed: 06/25/2024] Open
Abstract
OBJECTIVES This systematic review and meta-analysis aimed to assess the stroke detection performance of artificial intelligence (AI) in magnetic resonance imaging (MRI), and additionally to identify reporting insufficiencies. METHODS PRISMA guidelines were followed. MEDLINE, Embase, Cochrane Central, and IEEE Xplore were searched for studies utilising MRI and AI for stroke detection. The protocol was prospectively registered with PROSPERO (CRD42021289748). Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were the primary outcomes. Only studies using MRI in adults were included. The intervention was AI for stroke detection with ischaemic and haemorrhagic stroke in separate categories. Any manual labelling was used as a comparator. A modified QUADAS-2 tool was used for bias assessment. The minimum information about clinical artificial intelligence modelling (MI-CLAIM) checklist was used to assess reporting insufficiencies. Meta-analyses were performed for sensitivity, specificity, and hierarchical summary ROC (HSROC) on low risk of bias studies. RESULTS Thirty-three studies were eligible for inclusion. Fifteen studies had a low risk of bias. Low-risk studies were better for reporting MI-CLAIM items. Only one study examined a CE-approved AI algorithm. Forest plots revealed detection sensitivity and specificity of 93% and 93% with identical performance in the HSROC analysis and positive and negative likelihood ratios of 12.6 and 0.079. CONCLUSION Current AI technology can detect ischaemic stroke in MRI. There is a need for further validation of haemorrhagic detection. The clinical usability of AI stroke detection in MRI is yet to be investigated. CRITICAL RELEVANCE STATEMENT This first meta-analysis concludes that AI, utilising diffusion-weighted MRI sequences, can accurately aid the detection of ischaemic brain lesions and its clinical utility is ready to be uncovered in clinical trials. KEY POINTS There is a growing interest in AI solutions for detection aid. The performance is unknown for MRI stroke assessment. AI detection sensitivity and specificity were 93% and 93% for ischaemic lesions. There is limited evidence for the detection of patients with haemorrhagic lesions. AI can accurately detect patients with ischaemic stroke in MRI.
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Affiliation(s)
- Jonas Asgaard Bojsen
- Research and Innovation Unit of Radiology, Odense University Hospital, University of Southern Denmark, Odense, Denmark.
| | - Mohammad Talal Elhakim
- Research and Innovation Unit of Radiology, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Ole Graumann
- Research Unit of Radiology, Aarhus University Hospital, Aarhus University, Aarhus, Denmark
| | - David Gaist
- Research Unit for Neurology, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Mads Nielsen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Frederik Severin Gråe Harbo
- Research and Innovation Unit of Radiology, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Christian Hedeager Krag
- Radiological AI Test Center, Copenhagen University Hospital-Bispebjerg, Frederiksberg, Herlev and Gentofte Hospital, Copenhagen, Denmark
- Department of Radiology, Copenhagen University Hospital-Herlev and Gentofte, Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Malini Vendela Sagar
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital-Herlev and Gentofte, Copenhagen, Denmark
| | - Christina Kruuse
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Mikael Ploug Boesen
- Radiological AI Test Center, Copenhagen University Hospital-Bispebjerg, Frederiksberg, Herlev and Gentofte Hospital, Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Radiology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Benjamin Schnack Brandt Rasmussen
- Research and Innovation Unit of Radiology, Odense University Hospital, University of Southern Denmark, Odense, Denmark
- Centre for Clinical Artificial Intelligence, Odense University Hospital, University of Southern Denmark, Odense, Denmark
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Abd-Ellah MK, Awad AI, Khalaf AAM, Ibraheem AM. Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks. Sci Rep 2024; 14:9501. [PMID: 38664436 PMCID: PMC11045751 DOI: 10.1038/s41598-024-59566-7] [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: 09/09/2023] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.
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Affiliation(s)
| | - Ali Ismail Awad
- College of Information Technology, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates.
- Faculty of Engineering, Al-Azhar University, P.O. Box 83513, Qena, Egypt.
| | - Ashraf A M Khalaf
- Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61519, Egypt
| | - Amira Mofreh Ibraheem
- Faculty of Artificial Intelligence, Egyptian Russian University, Cairo, 11829, Egypt
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Wiem T, Ali D. Deep second generation wavelet autoencoders based on curvelet pooling for brain pathology classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Krishnapriya S, Karuna Y. Pre-trained deep learning models for brain MRI image classification. Front Hum Neurosci 2023; 17:1150120. [PMID: 37151901 PMCID: PMC10157370 DOI: 10.3389/fnhum.2023.1150120] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/06/2023] [Indexed: 05/09/2023] Open
Abstract
Brain tumors are serious conditions caused by uncontrolled and abnormal cell division. Tumors can have devastating implications if not accurately and promptly detected. Magnetic resonance imaging (MRI) is one of the methods frequently used to detect brain tumors owing to its excellent resolution. In the past few decades, substantial research has been conducted in the field of classifying brain images, ranging from traditional methods to deep-learning techniques such as convolutional neural networks (CNN). To accomplish classification, machine-learning methods require manually created features. In contrast, CNN achieves classification by extracting visual features from unprocessed images. The size of the training dataset had a significant impact on the features that CNN extracts. The CNN tends to overfit when its size is small. Deep CNNs (DCNN) with transfer learning have therefore been developed. The aim of this work was to investigate the brain MR image categorization potential of pre-trained DCNN VGG-19, VGG-16, ResNet50, and Inception V3 models using data augmentation and transfer learning techniques. Validation of the test set utilizing accuracy, recall, Precision, and F1 score showed that the pre-trained VGG-19 model with transfer learning exhibited the best performance. In addition, these methods offer an end-to-end classification of raw images without the need for manual attribute extraction.
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Devi S, Bakshi S, Sahoo MN. Effect of situational and instrumental distortions on the classification of brain MR images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Wang W, Zhang X, Wang SH, Zhang YD. Covid-19 Diagnosis by WE-SAJ. SYSTEMS SCIENCE & CONTROL ENGINEERING 2022; 10:325-335. [PMID: 36568847 PMCID: PMC7613983 DOI: 10.1080/21642583.2022.2045645] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 02/19/2022] [Indexed: 05/31/2023]
Abstract
With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and Fowlkes-Mallows Index of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.
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Affiliation(s)
- Wei Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Xin Zhang
- Department of Medical Imaging, The Fourth People's Hospital of Huai'an, Huai'an, Jiangsu Province, 223002, China
| | - Shui-Hua Wang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
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A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120783. [PMID: 36550989 PMCID: PMC9774129 DOI: 10.3390/bioengineering9120783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/01/2022] [Accepted: 12/04/2022] [Indexed: 12/14/2022]
Abstract
A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images.
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Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Shoeibi A, Khosravi A, Ling SH, Subasi A, Alizadehsani R, Gorriz JM, Abdulla SA, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front Mol Neurosci 2022; 15:999605. [PMID: 36267703 PMCID: PMC9577321 DOI: 10.3389/fnmol.2022.999605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022] Open
Abstract
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
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Affiliation(s)
- Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Navid Ghassemi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Delaram Sadeghi
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Abdulhamit Subasi
- Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Juan M. Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Sara A. Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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9
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An attention-guided convolutional neural network for automated classification of brain tumor from MRI. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07742-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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10
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Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface. NANOMATERIALS 2022; 12:nano12050878. [PMID: 35269366 PMCID: PMC8912297 DOI: 10.3390/nano12050878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 02/04/2023]
Abstract
Little is known about the rising impacts of Coriolis force and volume fraction of nanoparticles in industrial, mechanical, and biological domains, with an emphasis on water conveying 47 nm nanoparticles of alumina nanoparticles. We explored the impact of the volume fraction and rotation parameter on water conveying 47 nm of alumina nanoparticles across a uniform surface in this study. The Levenberg–Marquardt backpropagated neural network (LMB-NN) architecture was used to examine the transport phenomena of 47 nm conveying nanoparticles. The partial differential equations (PDEs) are converted into a system of Ordinary Differential Equations (ODEs). To assess our soft-computing process, we used the RK4 method to acquire reference solutions. The problem is investigated using two situations, each with three sub-cases for the change of the rotation parameter K and the volume fraction ϕ. Our simulation results are compared to the reference solutions. It has been proven that our technique is superior to the current state-of-the-art. For further explanation, error histograms, regression graphs, and fitness values are graphically displayed.
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Tripathi PC, Bag S. A computer-aided grading of glioma tumor using deep residual networks fusion. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 215:106597. [PMID: 34974232 DOI: 10.1016/j.cmpb.2021.106597] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 10/19/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Among different cancer types, glioma is considered as a potentially fatal brain cancer that arises from glial cells. Early diagnosis of glioma helps the physician in offering effective treatment to the patients. Magnetic Resonance Imaging (MRI)-based Computer-Aided Diagnosis for the brain tumors has attracted a lot of attention in the literature in recent years. In this paper, we propose a novel deep learning-based computer-aided diagnosis for glioma tumors. METHODS The proposed method incorporates a two-level classification of gliomas. Firstly, the tumor is classified into low-or high-grade and secondly, the low-grade tumors are classified into two types based on the presence of chromosome arms 1p/19q. The feature representations of four residual networks have been used to solve the problem by utilizing transfer learning approach. Furthermore, we have fused these trained models using a novel Dempster-shafer Theory (DST)-based fusion scheme in order to enhance the classification performance. Extensive data augmentation strategies are also utilized to avoid over-fitting of the discrimination models. RESULTS Extensive experiments have been performed on an MRI dataset to show the effectiveness of the method. It has been found that our method achieves 95.87% accuracy for glioma classification. The results obtained by our method have also been compared with different existing methods. The comparative study reveals that our method not only outperforms traditional machine learning-based methods, but it also produces better results to state-of-the-art deep learning-based methods. CONCLUSION The fusion of different residual networks enhances the tumor classification performance. The experimental findings indicates that Dempster-shafer Theory (DST)-based fusion technique produces superior performance in comparison to other fusion schemes.
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Affiliation(s)
- Prasun Chandra Tripathi
- Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines) Dhanabd, Dhanbad 826004, India.
| | - Soumen Bag
- Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines) Dhanabd, Dhanbad 826004, India.
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An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:8608305. [PMID: 34917168 PMCID: PMC8670911 DOI: 10.1155/2021/8608305] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 11/19/2021] [Indexed: 12/03/2022]
Abstract
In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In the preprocessing stage, the median filter has been used in order to remove salt-and-pepper noise because MRI images are normally affected by this type of noise, the grayscale images are also converted to RGB images in this stage. In the preprocessing stage, the histogram equalization has also been used to enhance the quality of each RGB channel. In the feature extraction stage, the three channels, namely, red, green, and blue, are extracted from the RGB images and statistical measures, namely, mean, variance, skewness, kurtosis, entropy, energy, contrast, homogeneity, and correlation, are calculated for each channel; hence, a total of 27 features, 9 for each channel, are extracted from an RGB image. After the feature extraction stage, different machine learning algorithms, such as artificial neural network, k-nearest neighbors' algorithm, decision tree, and Naïve Bayes classifiers, have been applied in the classification stage on the features extracted in the feature extraction stage. We recorded the results with all these algorithms and found that the decision tree results are better as compared to the other classification algorithms which are applied on these features. Hence, we have considered decision tree for further processing. We have also compared the results of the proposed method with some well-known algorithms in terms of simplicity and accuracy; it was noted that the proposed method outshines the existing methods.
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Hu R, Peng Z, Zhu X, Gan J, Zhu Y, Ma J, Wu G. Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3843-3855. [PMID: 34310294 PMCID: PMC8931676 DOI: 10.1109/tmi.2021.3099641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the l1 -SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer's Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions. The source code can be visited by the url https://github.com/reynard-hu/mbbna.
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Fayaz M, Torokeldiev N, Turdumamatov S, Qureshi MS, Qureshi MB, Gwak J. An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network. SENSORS 2021; 21:s21227480. [PMID: 34833556 PMCID: PMC8619601 DOI: 10.3390/s21227480] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/01/2021] [Accepted: 11/08/2021] [Indexed: 12/21/2022]
Abstract
In this paper, a model based on discrete wavelet transform and convolutional neural network for brain MR image classification has been proposed. The proposed model is comprised of three main stages, namely preprocessing, feature extraction, and classification. In the preprocessing, the median filter has been applied to remove salt-and-pepper noise from the brain MRI images. In the discrete wavelet transform, discrete Harr wavelet transform has been used. In the proposed model, 3-level Harr wavelet decomposition has been applied on the images to remove low-level detail and reduce the size of the images. Next, the convolutional neural network has been used for classifying the brain MR images into normal and abnormal. The convolutional neural network is also a prevalent classification method and has been widely used in different areas. In this study, the convolutional neural network has been used for brain MRI classification. The proposed methodology has been applied to the standard dataset, and for performance evaluation, we have used different performance evaluation measures. The results indicate that the proposed method provides good results with 99% accuracy. The proposed method results are then presented for comparison with some state-of-the-art algorithms where simply the proposed method outperforms the counterpart algorithms. The proposed model has been developed to be used for practical applications.
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Affiliation(s)
- Muhammad Fayaz
- Department of Computer Science, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan; (M.F.); (M.S.Q.)
| | - Nurlan Torokeldiev
- Department of Mathematics and Natural Sciences, University of Central Asia, Khorog 736, Tajikistan;
| | - Samat Turdumamatov
- Department of Mathematics and Natural Sciences, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan;
| | - Muhammad Shuaib Qureshi
- Department of Computer Science, University of Central Asia, 310 Lenin Street, Naryn 722918, Kyrgyzstan; (M.F.); (M.S.Q.)
| | - Muhammad Bilal Qureshi
- Department of Computer Science and IT, University of Lakki Marwat, Lakki Marwat 28420, KPK, Pakistan;
| | - Jeonghwan Gwak
- Department of Software, Korea National University of Transportation, Chungju 27469, Korea
- Department of Biomedical Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of AI Robotics Engineering, Korea National University of Transportation, Chungju 27469, Korea
- Department of IT & Energy Convergence (BK21 FOUR), Korea National University of Transportation, Chungju 27469, Korea
- Correspondence: ; Tel.: +82-43-841-5852
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Abstract
AbstractBrain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.
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Fu G, Li J, Wang R, Ma Y, Chen Y. Attention-based full slice brain CT image diagnosis with explanations. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.04.044] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Song G, Shan T, Bao M, Liu Y, Zhao Y, Chen B. Automatic brain tumour diagnostic method based on a back propagation neural network and an extended set-membership filter. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106188. [PMID: 34229998 DOI: 10.1016/j.cmpb.2021.106188] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 05/10/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Diagnosing brain tumours remains a challenging task in clinical practice. Despite their questionable accuracy, magnetic resonance image (MRI) scans are presently considered the optimal facility for assessing the growth of tumours. However, the efficiency of manual diagnosis is low, and high computational cost and poor convergence restrict the application of machine learning methods. This study aims to design a method that can reliably diagnose brain tumours from MRI scans. METHODS First, image pre-processing (which includes background removal, size standardization, noise removal, and contrast enhancement) is utilized to normalize the images. Then, grey level co-occurrence matrix features are selected as texture features of the brain MRI scans. Finally, a method combining a back propagation neural network (BPNN) and an extended set-membership filter (ESMF) is proposed to classify features and perform image classification. RESULTS A total of 304 patient MRI series (247 images of brains with tumours and 57 images of normal brains) were included and assessed in this study. The results revealed that our proposed method can achieve an accuracy of 95.40% and has classification accuracies of 97.14% and 88.24% for brain tumour and normal brain, respectively. CONCLUSION This study proposes an automatic brain tumour detection model constructed using a combination of BPNN and ESMF. The model is found to be able to accurately classify brain MRI scans as normal or tumour images.
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Affiliation(s)
- Guoli Song
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Tian Shan
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Min Bao
- Shengjing Hospital of China Medical University, Shenyang 110011 China; Liaoning Medical Surgery and Rehabilitation Robot Engineering Research Center, Shenyang 110134, China
| | - Yunhui Liu
- Shengjing Hospital of China Medical University, Shenyang 110011 China; Liaoning Medical Surgery and Rehabilitation Robot Engineering Research Center, Shenyang 110134, China
| | - Yiwen Zhao
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
| | - Baoshi Chen
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
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Lu SY, Wang SH, Zhang YD. A classification method for brain MRI via MobileNet and feedforward network with random weights. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.10.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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19
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Lu S, Wang SH, Zhang YD. Detecting pathological brain via ResNet and randomized neural networks. Heliyon 2020; 6:e05625. [PMID: 33305056 PMCID: PMC7711146 DOI: 10.1016/j.heliyon.2020.e05625] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 03/01/2020] [Accepted: 11/25/2020] [Indexed: 01/14/2023] Open
Abstract
Brain disease is one of the leading causes of death nowadays. Medical imaging is the most effective method for brain disease diagnosis, which provides a clear view of the interior brain. However, manual interpretation requires too much time and effort because medical images contain a large volume of information. Computer aided diagnosis is playing a more and more significant role in the clinic, which can help doctors and physicians to analyze medical images automatically. In this study, a novel pathological brain detection system was proposed for brain magnetic resonance images based on ResNet and randomized neural networks. Firstly, a ResNet was employed as the feature extractor, which was a famous convolutional neural network structure. Then, we used three randomized neural networks, i.e., the Schmidt neural network, the random vector functional-link net, and the extreme learning machine. The weights and biases in the three networks were trained by the chaotic bat algorithm. The three proposed methods achieved similar results based on five runs, and they yielded comparable performance in comparison with state-of-the-art approaches.
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Affiliation(s)
- Siyuan Lu
- School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
| | - Shui-Hua Wang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Corresponding author.
| | - Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
- Corresponding author.
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Li J, Fu G, Chen Y, Li P, Liu B, Pei Y, Feng H. A multi-label classification model for full slice brain computerised tomography image. BMC Bioinformatics 2020; 21:200. [PMID: 33203366 PMCID: PMC7672970 DOI: 10.1186/s12859-020-3503-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 04/16/2020] [Indexed: 12/04/2022] Open
Abstract
Background Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. Results In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. Conclusion The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.
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Affiliation(s)
- Jianqiang Li
- School of Software Engineering, Beijing University of Technology, Beijing, 100124, China.,Beijing Engineering Research Center for IoT Software and Systems, Beijing, 100124, China
| | - Guanghui Fu
- School of Software Engineering, Beijing University of Technology, Beijing, 100124, China.,Beijing Engineering Research Center for IoT Software and Systems, Beijing, 100124, China
| | - Yueda Chen
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, 300350, China
| | - Pengzhi Li
- School of Software Engineering, Beijing University of Technology, Beijing, 100124, China.,Beijing Engineering Research Center for IoT Software and Systems, Beijing, 100124, China
| | - Bo Liu
- School of Software Engineering, Beijing University of Technology, Beijing, 100124, China.,Beijing Engineering Research Center for IoT Software and Systems, Beijing, 100124, China
| | - Yan Pei
- Computer Science Division, University of Aizu, Aizuwakamatsu, 965-8580, Japan.
| | - Hui Feng
- School of Software Engineering, Beijing University of Technology, Beijing, 100124, China.,Beijing Engineering Research Center for IoT Software and Systems, Beijing, 100124, China
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21
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Gupta T, Gandhi TK, Gupta R, Panigrahi B. Classification of patients with tumor using MR FLAIR images. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2017.10.037] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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22
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Nayak DR, Dash R, Majhi B. Automated diagnosis of multi-class brain abnormalities using MRI images: A deep convolutional neural network based method. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.04.018] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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23
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Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155135] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.
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Devi S, Sahoo MN, Bakshi S. A novel privacy-supporting 2-class classification technique for brain MRI images. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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25
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Lu S, Wang SH, Zhang YD. Detection of abnormal brain in MRI via improved AlexNet and ELM optimized by chaotic bat algorithm. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05082-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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26
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Muduli D, Dash R, Majhi B. Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101912] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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27
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A deep stacked random vector functional link network autoencoder for diagnosis of brain abnormalities and breast cancer. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101860] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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28
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Gudigar A, Raghavendra U, Hegde A, Kalyani M, Ciaccio EJ, Rajendra Acharya U. Brain pathology identification using computer aided diagnostic tool: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 187:105205. [PMID: 31786457 DOI: 10.1016/j.cmpb.2019.105205] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2019] [Revised: 11/12/2019] [Accepted: 11/12/2019] [Indexed: 05/28/2023]
Abstract
Computer aided diagnostic (CAD) has become a significant tool in expanding patient quality-of-life by reducing human errors in diagnosis. CAD can expedite decision-making on complex clinical data automatically. Since brain diseases can be fatal, rapid identification of brain pathology to prolong patient life is an important research topic. Many algorithms have been proposed for efficient brain pathology identification (BPI) over the past decade. Constant refinement of the various image processing algorithms must take place to expand performance of the automatic BPI task. In this paper, a systematic survey of contemporary BPI algorithms using brain magnetic resonance imaging (MRI) is presented. A summarization of recent literature provides investigators with a helpful synopsis of the domain. Furthermore, to enhance the performance of BPI, future research directions are indicated.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
| | - Ajay Hegde
- Neurosurgery, Institute of Neurological Sciences, NHS Greater Glasgow and Clyde, Glasgow, United Kingdom
| | - M Kalyani
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, United States
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Clementi 599491, Singapore; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
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29
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An automated computer-aided diagnosis system for classification of MR images using texture features and gbest-guided gravitational search algorithm. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.03.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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30
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Melingi SB, Vijayalakshmi V. A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm. Curr Med Imaging 2020; 15:170-183. [PMID: 31975663 DOI: 10.2174/1573405614666180209150338] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 01/17/2018] [Accepted: 01/29/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum. OBJECTIVE The main objective of this paper is to segment the ischemic stroke lesions in Magnetic Resonance (MR) images in the presence of other pathologies like neurological disorder, encephalopathy, brain damage, Multiple sclerosis (MS). METHODS In this paper, we utilize a hybrid way to deal with segment the ischemic stroke from alternate pathologies in magnetic resonance (MR) images utilizing Random Decision Forest (RDF) and Gravitational Search Algorithm (GSA). The RDF approach is an effective machine learning approach. RESULTS The RDF strategy joins two parameters; they are; the number of trees in the forest and the number of leaves per tree; it runs quickly and proficiently when dealing with vast data. The GSA algorithm is utilized to optimize the RDF data for choosing the best number of trees and the number of leaves per tree in the forest. CONCLUSION This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method.
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Affiliation(s)
- Sunil Babu Melingi
- Department of Electronics and Communication Engineering, Pondicherry Engineering College (PEC), Puducherry, India
| | - V Vijayalakshmi
- Department of Electronics and Communication Engineering, Pondicherry Engineering College (PEC), Puducherry, India
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31
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Nayak DR, Dash R, Majhi B, Acharya UR. Application of fast curvelet Tsallis entropy and kernel random vector functional link network for automated detection of multiclass brain abnormalities. Comput Med Imaging Graph 2019; 77:101656. [PMID: 31563069 DOI: 10.1016/j.compmedimag.2019.101656] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 07/11/2019] [Accepted: 08/22/2019] [Indexed: 12/18/2022]
Abstract
Binary classification of brain magnetic resonance (MR) images has made remarkable progress and many automated systems have been developed in the last decade. Multiclass classification of brain MR images is comparatively more challenging and has great clinical significance. Hence, it has recently become an active area of research in biomedical image processing. In this paper, an automated multiclass brain MR classification framework is proposed to categorize the MR images into five classes such as brain stroke, degenerative disease, infectious disease, brain tumor, and normal brain. A texture based feature descriptor is proposed using curvelet transform and Tsallis entropy to extract salient features from MR images. The potential of Tsallis entropy features is compared with Shannon entropy features. A kernel extension of random vector functional link network (KRVFL) is used to perform multiclass classification and improve the generalization performance at faster training speed. To validate the proposed method, two standard multiclass brain MR datasets (MD-1 and MD-2) are used. The proposed system obtained classification accuracies of 97.33% and 94.00% for MD-1 and MD-2 datasets respectively using 5-fold cross validation approach. The experimental results demonstrated the effectiveness of our system compared to the state-of-the-art schemes and hence, can be utilized as a supportive tool by physicians to verify their screening.
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Affiliation(s)
- Deepak Ranjan Nayak
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram 600127, India.
| | - Ratnakar Dash
- Department of Computer Science and Engineering, National Institute of Technology Rourkela, 769008, India.
| | - Banshidhar Majhi
- Department of Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram 600127, India.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599491, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, Malaysia.
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Abd-Ellah MK, Awad AI, Khalaf AAM, Hamed HFA. A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magn Reson Imaging 2019; 61:300-318. [PMID: 31173851 DOI: 10.1016/j.mri.2019.05.028] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 05/20/2019] [Accepted: 05/20/2019] [Indexed: 12/21/2022]
Abstract
The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.
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Affiliation(s)
- Mahmoud Khaled Abd-Ellah
- Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt.
| | - Ali Ismail Awad
- Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå 97187, Sweden; Faculty of Engineering, Al-Azhar University, P.O. Box 83513, Qena, Egypt.
| | - Ashraf A M Khalaf
- Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
| | - Hesham F A Hamed
- Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
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33
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Talo M, Baloglu UB, Yıldırım Ö, Rajendra Acharya U. Application of deep transfer learning for automated brain abnormality classification using MR images. COGN SYST RES 2019. [DOI: 10.1016/j.cogsys.2018.12.007] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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34
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Multi class disorder detection of magnetic resonance brain images using composite features and neural network. Biomed Eng Lett 2019; 9:221-231. [PMID: 31168427 DOI: 10.1007/s13534-019-00103-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 01/16/2019] [Accepted: 02/07/2019] [Indexed: 01/02/2023] Open
Abstract
Brain disorder recognition has becoming a promising area of study. In reality, some disorders share similar features and signs, making the task of diagnosis and treatment challenging. This paper presents a rigorous and robust computer aided diagnosis system for the detection of multiple brain abnormalities which can assist physicians in the diagnosis and treatment of brain diseases. In this system, we used energy of wavelet sub bands, textural features of gray level co-occurrence matrix and intensity feature of MR brain images. These features are ranked using Wilcoxon test. The composite features are classified using back propagation neural network. Bayesian regulation is adopted to find the optimal weights of neural network. The experimentation is carried out on datasets DS-90 and DS-310 of Harvard Medical School. To enhance the generalization capability of the network, fivefold stratified cross validation technique is used. The proposed system yields multi class disease classification accuracy of 100% in differentiating 90 MR brain images into 18 classes and 97.81% in differentiating 310 MR brain images into 6 classes. The experimental results reveal that the composite features along with BPNN classifier create a competent and reliable system for the identification of multiple brain disorders which can be used in clinical applications. The Wilcoxon test outcome demonstrates that standard deviation feature along with energies of approximate and vertical sub bands of level 7 contribute the most in achieving enhanced multi class classification performance results.
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35
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Ayadi W, Elhamzi W, Charfi I, Atri M. A hybrid feature extraction approach for brain MRI classification based on Bag-of-words. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.10.010] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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36
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Kabir Anaraki A, Ayati M, Kazemi F. Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.10.004] [Citation(s) in RCA: 188] [Impact Index Per Article: 37.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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37
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Ayalapogu RR, Pabboju S, Ramisetty RR. Analysis of dual tree M-band wavelet transform based features for brain image classification. Magn Reson Med 2018; 80:2393-2401. [PMID: 29707806 DOI: 10.1002/mrm.27210] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 03/15/2018] [Accepted: 03/15/2018] [Indexed: 02/06/2023]
Abstract
PURPOSE The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment. METHODS In this study, an efficient computer-aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m-band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with k-fold approach. RESULTS Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4th level statistical features of DTMBWT. CONCLUSION Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification.
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Affiliation(s)
| | - Suresh Pabboju
- Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India
| | - Rajeswara Rao Ramisetty
- Jawaharal Nehru Technological University, University College of Engineering Vizianagaram, Andhra Pradesh, India
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38
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Nayak DR, Dash R, Majhi B. Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.12.030] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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39
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Iqbal S, Khan MUG, Saba T, Rehman A. Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett 2018; 8:5-28. [PMID: 30603187 PMCID: PMC6208555 DOI: 10.1007/s13534-017-0050-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 08/15/2017] [Accepted: 09/21/2017] [Indexed: 12/16/2022] Open
Abstract
Medical imaging plays an integral role in the identification, segmentation, and classification of brain tumors. The invention of MRI has opened new horizons for brain-related research. Recently, researchers have shifted their focus towards applying digital image processing techniques to extract, analyze and categorize brain tumors from MRI. Categorization of brain tumors is defined in a hierarchical way moving from major to minor ones. A plethora of work could be seen in literature related to the classification of brain tumors in categories such as benign and malignant. However, there are only a few works reported on the multiclass classification of brain images where each part of the image containing tumor is tagged with major and minor categories. The precise classification is difficult to achieve due to ambiguities in images and overlapping characteristics of different type of tumors. In the current study, a comprehensive review of recent research on brain tumors multiclass classification using MRI is provided. These multiclass classification studies are categorized into two major groups: XX and YY and each group are further divided into three sub-groups. A set of common parameters from the reviewed works is extracted and compared to highlight the merits and demerits of individual works. Based on our analysis, we provide a set of recommendations for researchers and professionals working in the area of brain tumors classification.
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Affiliation(s)
- Sajid Iqbal
- Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan
| | - M. Usman Ghani Khan
- Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, Pakistan
| | - Tanzila Saba
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586 Saudi Arabia
| | - Amjad Rehman
- College of Computer and Information Systems, Al-Yamamah University, Riyadh, 11512 Saudi Arabia
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Jiang Q, Shen Y, Li H, Xu F. New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network. SENSORS 2018; 18:s18020337. [PMID: 29364856 PMCID: PMC5855057 DOI: 10.3390/s18020337] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 01/12/2018] [Accepted: 01/22/2018] [Indexed: 11/16/2022]
Abstract
Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.
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Affiliation(s)
- Quansheng Jiang
- School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
- Suzhou Key Laboratory of Precision and Efficient Machining Technology, Suzhou 215009, China.
| | - Yehu Shen
- School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
- Suzhou Key Laboratory of Precision and Efficient Machining Technology, Suzhou 215009, China.
| | - Hua Li
- School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
- Suzhou Key Laboratory of Precision and Efficient Machining Technology, Suzhou 215009, China.
| | - Fengyu Xu
- College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
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Wang S, Phillips P, Yang J, Sun P, Zhang Y. Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients. ACTA ACUST UNITED AC 2017; 61:431-41. [PMID: 26913453 DOI: 10.1515/bmt-2015-0152] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Accepted: 01/18/2016] [Indexed: 12/30/2022]
Abstract
AIM To develop an automatic magnetic resonance (MR) brain classification that can assist physicians to make a diagnosis and reduce wrong decisions. METHOD This article investigated the binary particle swarm optimization (BPSO) approach and proposed its three new variants: BPSO with mutation and time-varying acceleration coefficients (BPSO-MT), BPSO with mutation (BPSO-M), and BPSO with time-varying acceleration coefficients (BPSO-T). We first extracted wavelet entropy (WE) features from both approximation and detail sub-bands of eight-level decomposition. Afterwards, we used the proposed BPSO-M, BPSO-T, and BPSO-MT to select features. Finally, the selected features were fed into a probabilistic neural network (PNN). RESULTS The proposed BPSO-MT performed better than BPSO-T and BPSO-M. It finally selected two features of entropies of the following two sub-bands (V1, D1). The proposed system "WE + BPSO-MT + PNN" yielded perfect classification on Data160 and Data66. In addition, it yielded 99.53% average accuracy for the Data255, over 10 repetitions of k-fold stratified cross validation (SCV), higher than state-of-the-art approaches. CONCLUSIONS The proposed method is effective for MR brain classification.
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Gupta T, Gandhi TK, Panigrahi B. Multi-sequential MR brain image classification for tumor detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-169293] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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43
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Vidyarthi A, Mittal N. Texture based feature extraction method for classification of brain tumor MRI. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2017. [DOI: 10.3233/jifs-169223] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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44
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A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2017. [DOI: 10.1155/2017/9571262] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
This paper presents a two-dimensional wavelet based decomposition algorithm for classification of biomedical images. The two-dimensional wavelet decomposition is done up to five levels for the input images. Histograms of decomposed images are then used to form the feature set. This feature set is further reduced using probabilistic principal component analysis. The reduced set of features is then fed into either K nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. The proposed algorithm has been found better up to 3.3%, 12.75%, and 13.75% on average over the first, second, and third algorithm, respectively, using KNN and up to 6.22%, 13.9%, and 14.1% on average using ANN. The dataset used for comparison consisted of CT Scan images of lungs and MR images of heart as obtained from different sources.
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45
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Wang S, Zhang Y, Liu G, Phillips P, Yuan TF. Detection of Alzheimer's Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging. J Alzheimers Dis 2016; 50:233-48. [PMID: 26682696 DOI: 10.3233/jad-150848] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Within the past decade, computer scientists have developed many methods using computer vision and machine learning techniques to detect Alzheimer's disease (AD) in its early stages. OBJECTIVE However, some of these methods are unable to achieve excellent detection accuracy, and several other methods are unable to locate AD-related regions. Hence, our goal was to develop a novel AD brain detection method. METHODS In this study, our method was based on the three-dimensional (3D) displacement-field (DF) estimation between subjects in the healthy elder control group and AD group. The 3D-DF was treated with AD-related features. The three feature selection measures were used in the Bhattacharyya distance, Student's t-test, and Welch's t-test (WTT). Two non-parallel support vector machines, i.e., generalized eigenvalue proximal support vector machine and twin support vector machine (TSVM), were then used for classification. A 50 × 10-fold cross validation was implemented for statistical analysis. RESULTS The results showed that "3D-DF+WTT+TSVM" achieved the best performance, with an accuracy of 93.05 ± 2.18, a sensitivity of 92.57 ± 3.80, a specificity of 93.18 ± 3.35, and a precision of 79.51 ± 2.86. This method also exceled in 13 state-of-the-art approaches. Additionally, we were able to detect 17 regions related to AD by using the pure computer-vision technique. These regions include sub-gyral, inferior parietal lobule, precuneus, angular gyrus, lingual gyrus, supramarginal gyrus, postcentral gyrus, third ventricle, superior parietal lobule, thalamus, middle temporal gyrus, precentral gyrus, superior temporal gyrus, superior occipital gyrus, cingulate gyrus, culmen, and insula. These regions were reported in recent publications. CONCLUSIONS The 3D-DF is effective in AD subject and related region detection.
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Affiliation(s)
- Shuihua Wang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.,School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, China.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Yudong Zhang
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China.,Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Ge Liu
- Translational Imaging Division & MRI Unit, Columbia University & New York State Psychiatric Institute, New York, NY, USA
| | - Preetha Phillips
- School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV, USA
| | - Ti-Fei Yuan
- School of Computer Science and Technology & School of Psychology, Nanjing Normal University, Nanjing, Jiangsu, China
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Zhang Y, Ji G, Yang J, Wang S, Dong Z, Phillips P, Sun P. Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO. Technol Health Care 2016; 24 Suppl 2:S641-9. [DOI: 10.3233/thc-161191] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Yudong Zhang
- School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, China
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Genlin Ji
- School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, China
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Jiquan Yang
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Shuihua Wang
- School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, China
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu, China
| | - Zhengchao Dong
- Translational Imaging Division and MRI Unit, Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Preetha Phillips
- School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV, USA
| | - Ping Sun
- Department of Electrical Engineering, The City College of New York, CUNY, New York, NY, USA
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Zhang Y, Sun Y, Phillips P, Liu G, Zhou X, Wang S. A Multilayer Perceptron Based Smart Pathological Brain Detection System by Fractional Fourier Entropy. J Med Syst 2016; 40:173. [DOI: 10.1007/s10916-016-0525-2] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2016] [Accepted: 05/16/2016] [Indexed: 12/23/2022]
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48
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Nayak DR, Dash R, Majhi B. Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.11.034] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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49
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Sahu O, Anand V, Kanhangad V, Pachori RB. Classification of magnetic resonance brain images using bi-dimensional empirical mode decomposition and autoregressive model. Biomed Eng Lett 2016. [DOI: 10.1007/s13534-015-0208-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
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50
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Zhang Y, Yang J, Wang S, Dong Z, Phillips P. Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. J EXP THEOR ARTIF IN 2016. [DOI: 10.1080/0952813x.2015.1132274] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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