1
|
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.
Collapse
|
2
|
Zeng D, Zeng C, Zeng Z, Li S, Deng Z, Chen S, Bian Z, Ma J. Basis and current state of computed tomography perfusion imaging: a review. Phys Med Biol 2022; 67. [PMID: 35926503 DOI: 10.1088/1361-6560/ac8717] [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: 11/17/2021] [Accepted: 08/04/2022] [Indexed: 12/30/2022]
Abstract
Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.
Collapse
Affiliation(s)
- Dong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Cuidie Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhixiong Zeng
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sui Li
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhen Deng
- Department of Neurology, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Sijin Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Zhaoying Bian
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| | - Jianhua Ma
- School of Biomedical Engineering, Southern Medical University, Guangdong 510515, China; and Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology, Southern Medical University, Guangdong 510515, People's Republic of China
| |
Collapse
|
3
|
A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083709] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of the most popular social media sites for providing public sentiment towards events that occurred during the COVID-19 period is Twitter. This is because Twitter posts are short and constantly being generated. This paper presents a deep learning approach for sentiment analysis of Twitter data related to COVID-19 reviews. The proposed algorithm is based on an LSTM-RNN-based network and enhanced featured weighting by attention layers. This algorithm uses an enhanced feature transformation framework via the attention mechanism. A total of four class labels (sad, joy, fear, and anger) from publicly available Twitter data posted in the Kaggle database were used in this study. Based on the use of attention layers with the existing LSTM-RNN approach, the proposed deep learning approach significantly improved the performance metrics, with an increase of 20% in accuracy and 10% to 12% in precision but only 12–13% in recall as compared with the current approaches. Out of a total of 179,108 COVID-19-related tweets, tweets with positive, neutral, and negative sentiments were found to account for 45%, 30%, and 25%, respectively. This shows that the proposed deep learning approach is efficient and practical and can be easily implemented for sentiment classification of COVID-19 reviews.
Collapse
|
4
|
Ji S, Zhao W. Displacement voxelization to resolve mesh-image mismatch: Application in deriving dense white matter fiber strains. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 213:106528. [PMID: 34808529 PMCID: PMC8665149 DOI: 10.1016/j.cmpb.2021.106528] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 10/01/2021] [Accepted: 11/09/2021] [Indexed: 05/19/2023]
Abstract
BACKGROUND AND OBJECTIVE It is common to combine biomechanical modeling and medical images for multimodal analyses. However, mesh-image mismatch may occur that prevents direct information exchange. To eliminate mesh-image mismatch, we develop a simple but elegant displacement voxelization technique based on image voxel corner nodes to achieve voxel-wise strain. We then apply the technique to derive dense white matter fiber strains along whole-brain tractography (∼35 k fiber tracts consisting of ∼3.3 million sampling points) resulting from head impact. METHODS Displacements at image voxel corner nodes are first obtained from model simulation via scattered interpolation. Each voxel is then scaled linearly to form a unit hexahedral element. This allows convenient and efficient voxel-wise strain tensor calculation and displacement interpolation at arbitrary fiber sampling points via shape functions. Fiber strains from displacement interpolation are then compared with those from the commonly used strain tensor projection using either voxel- or element-wise strain tensors. RESULTS Based on a synthetic displacement field, fiber strains interpolated from voxelized displacement are considerably more accurate than those from strain tensor projection relative to the prescribed ground-truth (determinant of coefficient (R2) of 1.00 and root mean squared error (RMSE) of 0.01 vs. 0.87 and 0.10, respectively). For a set of real-world reconstructed head impacts (N = 53), the strain tensor projection method performs similarly poorly (R2 of 0.80-0.90 and RMSE of 0.03-0.07), with overestimation strongly correlated with strain magnitude (Pearson correlation coefficient >0.9). Up to ∼15% of the fiber strains are overestimated by more than the lower bound of a conservative injury threshold of 0.09. The percentage increases to ∼37% when halving the threshold. Voxel interpolation is also significantly more efficient (15 s vs. 40 s for element strain tensor projection, without parallelization). CONCLUSIONS Voxelized displacement interpolation is considerably more accurate and efficient in deriving dense white matter fiber strains than strain tensor projection. The latter generally overestimates with overestimation magnitude strongly correlating with fiber strain magnitude. Displacement voxelization is an effective technique to eliminate mesh-image mismatch and generates a convenient image representation of tissue deformation. This technique can be generalized to broadly facilitate a diverse range of image-related biomechanical problems for multimodal analyses. The convenient image format may also promote and facilitate biomechanical data sharing in the future.
Collapse
Affiliation(s)
- Songbai Ji
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA 01506, USA; Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.
| | - Wei Zhao
- Department of Biomedical Engineering, Worcester Polytechnic Institute, 60 Prescott Street, Worcester, MA 01506, USA
| |
Collapse
|
5
|
Hu N, Zhang T, Wu Y, Tang B, Li M, Song B, Gong Q, Wu M, Gu S, Lui S. Detecting brain lesions in suspected acute ischemic stroke with CT-based synthetic MRI using generative adversarial networks. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:35. [PMID: 35282087 PMCID: PMC8848363 DOI: 10.21037/atm-21-4056] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 11/26/2021] [Indexed: 02/05/2023]
Abstract
Background Difficulties in detecting brain lesions in acute ischemic stroke (AIS) have convinced researchers to use computed tomography (CT) to scan for and magnetic resonance imaging (MRI) to search for these lesions. This work aimed to develop a generative adversarial network (GAN) model for CT-to-MR image synthesis and evaluate reader performance with synthetic MRI (syn-MRI) in detecting brain lesions in suspected patients. Methods Patients with primarily suspected AIS were randomly assigned to the training (n=140) or testing (n=53) set. Emergency CT and follow-up MR images in the training set were used to develop a GAN model to generate syn-MR images from the CT data in the testing set. The standard reference was the manual segmentations of follow-up MR images. Image similarity was evaluated between syn-MRI and the ground truth using a 4-grade visual rating scale, the peak signal-to-noise ratio (PSNR), and the structural similarity index measure (SSIM). Reader performance with syn-MRI and CT was evaluated and compared on a per-patient (patient detection) and per-lesion (lesion detection) basis. Paired t-tests or Wilcoxon signed-rank tests were used to compare reader performance in lesion detection between the syn-MRI and CT data. Results Grade 2–4 brain lesions were observed on syn-MRI in 92.5% (49/53) of the patients, while the remaining syn-MRI data showed no lesions compared to the ground truth. The GAN model exhibited a weak PSNR of 24.30 dB but a favorable SSIM of 0.857. Compared with CT, syn-MRI led to an increase in the overall sensitivity from 38% (57/150) to 82% (123/150) in patient detection and from 4% (68/1,620) to 16% (262/1,620) in lesion detection (R=0.32, corrected P<0.001), but the specificity in patient detection decreased from 67% (6/9) to 33% (3/9). An additional 75% (70/93) of patients and 15% (77/517) of lesions missed on CT were detected on syn-MRI. Conclusions The GAN model holds potential for generating synthetic MR images from noncontrast CT data and thus could help sensitively detect individuals among patients with suspected AIS. However, the image similarity performance of the model needs to be improved, and further expert discrimination is strongly recommended.
Collapse
Affiliation(s)
- Na Hu
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Tianwei Zhang
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yifan Wu
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Biqiu Tang
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Minlong Li
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, Zigong Fourth People's Hospital, Zigong, China
| | - Bin Song
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Min Wu
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Shi Gu
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Su Lui
- Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| |
Collapse
|
6
|
Inamdar MA, Raghavendra U, Gudigar A, Chakole Y, Hegde A, Menon GR, Barua P, Palmer EE, Cheong KH, Chan WY, Ciaccio EJ, Acharya UR. A Review on Computer Aided Diagnosis of Acute Brain Stroke. SENSORS (BASEL, SWITZERLAND) 2021; 21:8507. [PMID: 34960599 PMCID: PMC8707263 DOI: 10.3390/s21248507] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/05/2021] [Accepted: 12/09/2021] [Indexed: 01/01/2023]
Abstract
Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.
Collapse
Affiliation(s)
- Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Udupi Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Yashas Chakole
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Ajay Hegde
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India; (A.H.); (G.R.M.)
| | - Girish R. Menon
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India; (A.H.); (G.R.M.)
| | - Prabal Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Elizabeth Emma Palmer
- School of Women’s and Children’s Health, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Research Imaging Centre, University of Malaya, Kuala Lumpur 59100, Malaysia;
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| |
Collapse
|
7
|
Wang L, Guo Y, Ye J, Pan Z, Hu P, Zhong X, Qiu F, Zhang D, Huang Z. Protective Effect of Piceatannol Against Cerebral Ischaemia-Reperfusion Injury Via Regulating Nrf2/HO-1 Pathway In Vivo and Vitro. Neurochem Res 2021; 46:1869-1880. [PMID: 34031841 DOI: 10.1007/s11064-021-03328-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 03/17/2021] [Accepted: 04/12/2021] [Indexed: 02/07/2023]
Abstract
Piceatannol is a natural plant-derived compound with protective effects against cardiovascular diseases. However, its effect on cerebral ischaemia-reperfusion injury (CIRI) induced by oxidative stress remains unclear. This study aimed to investigate piceatannol's antioxidation in CIRI. An in vitro oxygen-glucose deprivation followed by reoxygenation model was used and cell viability was measured. A middle cerebral artery occlusion followed by reperfusion model was used in vivo. Neurological function, encephalisation quotient, oedema, and volume of the cerebral infarction were then evaluated. The effects of piceatannol on histopathological findings, as well as the ultrastructure of the cortex, were analysed. The activity of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and lactate dehydrogenase (LDH) and the malondialdehyde (MDA) content was measured both in vitro and in vivo. Finally, the expression of nuclear factor erythroid-2-related factor 2 (Nrf2), hemeoxygenase-1 (HO-1), and nicotinamide adenine dinucleotide phosphate quinone oxidoreductase 1 (NQO1) in cerebral tissue was detected using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting. Our results demonstrated that cell viability in the piceatannol groups was increased. The SOD, GSH-Px activities were increased as LDH activity and MDA content decreased in the piceatannol groups both in vitro and in vivo, reflecting a decrease in oxidative stress. The neurological severity score and infarction volume in the piceatannol groups at doses of 10 and 20 mg/kg were lower than those of the model group. Furthermore, the damage seen on histopathological examination was partially attenuated by piceatannol. RT-qPCR and western blot analysis indicated that the expression of Nrf2, HO-1, and NQO1 were significantly increased by piceatannol. The results of the study demonstrate that piceatannol exerts a protective effect against CIRI.
Collapse
Affiliation(s)
- Lingfeng Wang
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China
| | - Ying Guo
- First School of Clinical Medicine, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China
| | - Jiayi Ye
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China
| | - Zeyue Pan
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China
| | - Peihao Hu
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China
| | - Xiaoming Zhong
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China.
| | - Fengmei Qiu
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China
| | - Danni Zhang
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China
| | - Zhen Huang
- College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China.
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, Zhejiang Chinese Medical University, 548 Binwen Road, Hangzhou, 311400, Zhejiang, China.
| |
Collapse
|
8
|
Shafaat O, Bernstock JD, Shafaat A, Yedavalli VS, Elsayed G, Gupta S, Sotoudeh E, Sair HI, Yousem DM, Sotoudeh H. Leveraging artificial intelligence in ischemic stroke imaging. J Neuroradiol 2021; 49:343-351. [PMID: 33984377 DOI: 10.1016/j.neurad.2021.05.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/02/2021] [Accepted: 05/03/2021] [Indexed: 11/30/2022]
Abstract
Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.
Collapse
Affiliation(s)
- Omid Shafaat
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Amir Shafaat
- Department of Mechanical Engineering, Arak University of Technology, Daneshgah St, 38181-41167 Arak, Iran.
| | - Vivek S Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Galal Elsayed
- Department of Neurosurgery, University of Alabama at Birmingham, 1960 6th Ave. S., Birmingham, AL 35233, USA.
| | - Saksham Gupta
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Ehsan Sotoudeh
- Department of Surgery, Iranian Hospital in Dubai, P.O.BOX: 2330, Al-Wasl Road, Dubai 2330, UAE.
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA; Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, 600 North Wolfe Street, Baltimore, MD 21287, USA.
| | - David M Yousem
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Houman Sotoudeh
- Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA.
| |
Collapse
|
9
|
Deep learning applications for IoT in health care: A systematic review. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100550] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
|
10
|
Sirsat MS, Fermé E, Câmara J. Machine Learning for Brain Stroke: A Review. J Stroke Cerebrovasc Dis 2020; 29:105162. [DOI: 10.1016/j.jstrokecerebrovasdis.2020.105162] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 07/08/2020] [Accepted: 07/11/2020] [Indexed: 12/29/2022] Open
|
11
|
Kumar A, Upadhyay N, Ghosal P, Chowdhury T, Das D, Mukherjee A, Nandi D. CSNet: A new DeepNet framework for ischemic stroke lesion segmentation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 193:105524. [PMID: 32417618 DOI: 10.1016/j.cmpb.2020.105524] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/07/2020] [Accepted: 04/27/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVES Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment. Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development, medical discrepancies, unavailability of datasets, and the requirement of several MRI modalities for imaging. In this paper, we propose a composite deep learning model primarily based on the self-similar fractal networks and the U-Net model for performing acute stroke diagnosis tasks automatically to assist as well as expedite the decision-making process of medical practitioners. METHODS We put forth a new deep learning architecture, the Classifier-Segmenter network (CSNet), involving a hybrid training strategy with a self-similar (fractal) U-Net model, explicitly designed to perform the task of segmentation. In fractal networks, the underlying design strategy is based on the repetitive generation of self-similar fractals in place of residual connections. The U-Net model exploits both spatial as well as semantic information along with parameter sharing for a faster and efficient training process. In this new architecture, we exploit the benefits of both by combining them into one hybrid training scheme and developing the concept of a cascaded architecture, which further enhances the model's accuracy by removing redundant parts from the Segmenter's input. Lastly, a voting mechanism has been employed to further enhance the overall segmentation accuracy. RESULTS The performance of the proposed architecture has been scrutinized against the existing state-of-the-art deep learning architectures applied to various biomedical image processing tasks by submission on the publicly accessible web platform provided by the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge. The experimental results demonstrate the superiority of the proposed method when compared to similar submitted strategies, both qualitatively and quantitatively in terms of some of the well known evaluation metrics, such as Accuracy, Dice-Coefficient, Recall, and Precision. CONCLUSIONS We believe that our method may find use as a handy tool for doctors to identify the location and extent of irreversibly damaged brain tissue, which is said to be a critical part of the decision-making process in case of an acute stroke.
Collapse
Affiliation(s)
- Amish Kumar
- Department of Computer Science and Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Neha Upadhyay
- Department of Computer Science and Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Palash Ghosal
- Department of Computer Science and Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Tamal Chowdhury
- Department of Electronics and Communication Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Dipayan Das
- Department of Electronics and Communication Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India
| | - Amritendu Mukherjee
- Department of Interventional Radiology, Rashid Hospital, Dubai-4545, United Arab Emirates
| | - Debashis Nandi
- Department of Computer Science and Engineering, National Institute of Technoloy Durgapur - 713209, West Bengal, India.
| |
Collapse
|
12
|
Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection. Biomed Eng Lett 2020; 10:333-344. [PMID: 32864172 DOI: 10.1007/s13534-020-00158-5] [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: 12/03/2019] [Revised: 05/05/2020] [Accepted: 05/13/2020] [Indexed: 01/16/2023] Open
Abstract
Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemic stroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segment the ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Steps followed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classification and optimal feature group (FG) selection. The first order features, gray level run length matrix features, gray level co-occurrence matrix features and Hu's moment features are extracted. Classification is done using logistic regression (LR), support vector machine classifier (SVMC), random forest classifier (RFC) and neural network classifier (NNC). This proposed approach effectively detects ischemic stroke lesion with a classification accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained by the LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.
Collapse
|
13
|
|
14
|
Computational Image Analysis of Nonenhanced Computed Tomography for Acute Ischaemic Stroke: A Systematic Review. J Stroke Cerebrovasc Dis 2020; 29:104715. [DOI: 10.1016/j.jstrokecerebrovasdis.2020.104715] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/16/2020] [Accepted: 01/28/2020] [Indexed: 11/23/2022] Open
|
15
|
AIBH: Accurate Identification of Brain Hemorrhage Using Genetic Algorithm Based Feature Selection and Stacking. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2020. [DOI: 10.3390/make2020005] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Brain hemorrhage is a type of stroke which is caused by a ruptured artery, resulting in localized bleeding in or around the brain tissues. Among a variety of imaging tests, a computerized tomography (CT) scan of the brain enables the accurate detection and diagnosis of a brain hemorrhage. In this work, we developed a practical approach to detect the existence and type of brain hemorrhage in a CT scan image of the brain, called Accurate Identification of Brain Hemorrhage, abbreviated as AIBH. The steps of the proposed method consist of image preprocessing, image segmentation, feature extraction, feature selection, and design of an advanced classification framework. The image preprocessing and segmentation steps involve removing the skull region from the image and finding out the region of interest (ROI) using Otsu’s method, respectively. Subsequently, feature extraction includes the collection of a comprehensive set of features from the ROI, such as the size of the ROI, centroid of the ROI, perimeter of the ROI, the distance between the ROI and the skull, and more. Furthermore, a genetic algorithm (GA)-based feature selection algorithm is utilized to select relevant features for improved performance. These features are then used to train the stacking-based machine learning framework to predict different types of a brain hemorrhage. Finally, the evaluation results indicate that the proposed predictor achieves a 10-fold cross-validation (CV) accuracy (ACC), precision (PR), Recall, F1-score, and Matthews correlation coefficient (MCC) of 99.5%, 99%, 98.9%, 0.989, and 0.986, respectively, on the benchmark CT scan dataset. While comparing AIBH with the existing state-of-the-art classification method of the brain hemorrhage type, AIBH provides an improvement of 7.03%, 7.27%, and 7.38% based on PR, Recall, and F1-score, respectively. Therefore, the proposed approach considerably outperforms the existing brain hemorrhage classification approach and can be useful for the effective prediction of brain hemorrhage types from CT scan images (The code and data can be found here: http://cs.uno.edu/~tamjid/Software/AIBH/code_data.zip).
Collapse
|
16
|
Afolabi AO, Toivanen P. Harmonization and Categorization of Metrics and Criteria for Evaluation of Recommender Systems in Healthcare From Dual Perspectives. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2020. [DOI: 10.4018/ijehmc.2020010105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Researchers' choice of metrics and criteria in evaluating recommender systems depends on what the researcher feels is popular among other researchers, or sometimes based on the objective of the research. There is no harmonized set of criteria and metrics that can be referenced when evaluating recommender systems in healthcare. In this article, a set of metrics and criteria are harmonized and categorized as a guide for evaluating recommender systems. By means of an online survey, the opinions of forty-four experienced researchers and other stakeholders from eight countries and four continents were sought on the relevance of identified metrics and criteria. Analysis of the results show speed and timeliness are at the top. Topping the list of criteria is the provision of information that will guide users to useful decisions. The result is presented from two logical perspectives. Four categories are then identified as a useful guide for evaluating recommender systems.
Collapse
Affiliation(s)
| | - Pekka Toivanen
- University of Eastern Finland, School of Computing, Kuopio, Finland
| |
Collapse
|
17
|
Sheth SA, Lopez-Rivera V, Barman A, Grotta JC, Yoo AJ, Lee S, Inam ME, Savitz SI, Giancardo L. Machine Learning-Enabled Automated Determination of Acute Ischemic Core From Computed Tomography Angiography. Stroke 2019; 50:3093-3100. [PMID: 31547796 DOI: 10.1161/strokeaha.119.026189] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background and Purpose- The availability of and expertise to interpret advanced neuroimaging recommended in the guideline-based endovascular stroke therapy (EST) evaluation are limited. Here, we develop and validate an automated machine learning-based method that evaluates for large vessel occlusion (LVO) and ischemic core volume in patients using a widely available modality, computed tomography angiogram (CTA). Methods- From our prospectively maintained stroke registry and electronic medical record, we identified patients with acute ischemic stroke and stroke mimics with contemporaneous CTA and computed tomography perfusion (CTP) with RAPID (IschemaView) post-processing as a part of the emergent stroke workup. A novel convolutional neural network named DeepSymNet was created and trained to identify LVO as well as infarct core from CTA source images, against CTP-RAPID definitions. Model performance was measured using 10-fold cross validation and receiver-operative curve area under the curve (AUC) statistics. Results- Among the 297 included patients, 224 (75%) had acute ischemic stroke of which 179 (60%) had LVO. Mean CTP-RAPID ischemic core volume was 23±42 mL. LVO locations included internal carotid artery (13%), M1 (44%), and M2 (21%). The DeepSymNet algorithm autonomously learned to identify the intracerebral vasculature on CTA and detected LVO with AUC 0.88. The method was also able to determine infarct core as defined by CTP-RAPID from the CTA source images with AUC 0.88 and 0.90 (ischemic core ≤30 mL and ≤50 mL). These findings were maintained in patients presenting in early (0-6 hours) and late (6-24 hours) time windows (AUCs 0.90 and 0.91, ischemic core ≤50 mL). DeepSymNet probabilities from CTA images corresponded with CTP-RAPID ischemic core volumes as a continuous variable with r=0.7 (Pearson correlation, P<0.001). Conclusions- These results demonstrate that the information needed to perform the neuroimaging evaluation for endovascular therapy with comparable accuracy to advanced imaging modalities may be present in CTA, and the ability of machine learning to automate the analysis.
Collapse
Affiliation(s)
- Sunil A Sheth
- From the Departments of Neurology (S.A.S., V.L.-R., S.L., S.I.S.), UTHealth McGovern Medical School, Houston, TX.,Institute for Stroke and Cerebrovascular Diseases (S.I.S., S.A.S., A.B., L.G.), UTHealth McGovern Medical School, Houston, TX
| | - Victor Lopez-Rivera
- From the Departments of Neurology (S.A.S., V.L.-R., S.L., S.I.S.), UTHealth McGovern Medical School, Houston, TX
| | - Arko Barman
- Institute for Stroke and Cerebrovascular Diseases (S.I.S., S.A.S., A.B., L.G.), UTHealth McGovern Medical School, Houston, TX.,Center for Precision Health, UTHealth School of Biomedical Informatics, Houston, TX (A.B., L.G.)
| | - James C Grotta
- Clinical Innovation and Research Institute, Memorial Hermann Hospital, Texas Medical Center, Houston (J.C.G.)
| | - Albert J Yoo
- Texas Stroke Institute, Dallas-Fort Worth (A.J.Y.)
| | - Songmi Lee
- From the Departments of Neurology (S.A.S., V.L.-R., S.L., S.I.S.), UTHealth McGovern Medical School, Houston, TX
| | - Mehmet E Inam
- Neurosurgery (M.E.I.), UTHealth McGovern Medical School, Houston, TX
| | - Sean I Savitz
- From the Departments of Neurology (S.A.S., V.L.-R., S.L., S.I.S.), UTHealth McGovern Medical School, Houston, TX.,Institute for Stroke and Cerebrovascular Diseases (S.I.S., S.A.S., A.B., L.G.), UTHealth McGovern Medical School, Houston, TX
| | - Luca Giancardo
- Diagnostic and Interventional Imaging (L.G.), UTHealth McGovern Medical School, Houston, TX.,Institute for Stroke and Cerebrovascular Diseases (S.I.S., S.A.S., A.B., L.G.), UTHealth McGovern Medical School, Houston, TX.,Center for Precision Health, UTHealth School of Biomedical Informatics, Houston, TX (A.B., L.G.)
| |
Collapse
|
18
|
Sarmento RM, Vasconcelos FFX, Filho PPR, Wu W, de Albuquerque VHC. Automatic Neuroimage Processing and Analysis in Stroke-A Systematic Review. IEEE Rev Biomed Eng 2019; 13:130-155. [PMID: 31449031 DOI: 10.1109/rbme.2019.2934500] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This article presents a systematic review of the current computational technologies applied to medical images for the detection, segmentation, and classification of strokes. Besides, analyzing and evaluating the technological advances, the challenges to be overcome and the future trends are discussed. The principal approaches make use of artificial intelligence, digital image processing and analysis, and various other technologies to develop computer-aided diagnosis (CAD) systems to improve the accuracy in the diagnostic process, as well as the interpretation consistency of medical images. However, there are some points that require greater attention such as low sensitivity, optimization of the algorithm, a reduction of false positives, and improvement in the identification and segmentation processes of different sizes and shapes. Also, there is a need to improve the classification steps of different stroke types and subtypes. Furthermore, there is an additional need for further research to improve the current techniques and develop new algorithms to overcome disadvantages identified here. The main focus of this research is to analyze the applied technologies for the development of CAD systems and verify how effective they are for stroke detection, segmentation, and classification. The main contributions of this review are that it analyzes only up-to-date studies, mainly from 2015 to 2018, as well as organizing the various studies in the area according to the research proposal, i.e., detection, segmentation, and classification of the types of stroke and the respective techniques used. Thus, the review has great relevance for future research, since it presents an ample comparison of the most recent works in the area, clearly showing the existing difficulties and the models that have been proposed to overcome such difficulties.
Collapse
|
19
|
Computer-Aided Detection of Hyperacute Stroke Based on Relative Radiomic Patterns in Computed Tomography. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9081668] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Ischemic stroke is one of the leading causes of disability and death. To achieve timely assessments, a computer-aided diagnosis (CAD) system was proposed to perform early recognition of hyperacute ischemic stroke based on non-contrast computed tomography (NCCT). In total, 26 patients with hyperacute ischemic stroke (with onset <6 h previous) and 56 normal controls composed the image database. For each NCCT slice, textural features were extracted from Ranklet-transformed images which had enhanced local contrast. Textural differences between the two sides of an image were calculated and combined in a machine learning classifier to detect stroke areas. The proposed CAD system using Ranklet features achieved significantly higher accuracy (81% vs. 71%), specificity (90% vs. 79%), and area under the curve (Az) (0.81 vs. 0.73) than conventional textural features. Diagnostic suggestions provided by the CAD system are fast and promising and could be useful in the pipeline of hyperacute ischemic stroke assessments.
Collapse
|
20
|
Combination of hand-crafted and unsupervised learned features for ischemic stroke lesion detection from Magnetic Resonance Images. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2019.01.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
21
|
da Nóbrega RVM, Rebouças Filho PP, Rodrigues MB, da Silva SPP, Dourado Júnior CMJM, de Albuquerque VHC. Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3895-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
22
|
Automated approach for detection of ischemic stroke using Delaunay Triangulation in brain MRI images. Comput Biol Med 2018; 103:116-129. [PMID: 30359807 DOI: 10.1016/j.compbiomed.2018.10.016] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 10/08/2018] [Accepted: 10/14/2018] [Indexed: 11/22/2022]
Abstract
It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic resonance imaging (MRI) images due to variation in different lesion sizes, variation in morphological structure, and similarity in intensity of lesion with normal brain in three types of stroke, namely partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). In this paper, we have integrated the advantages of Delaunay triangulation (DT) and fractional order Darwinian particle swarm optimization (FODPSO), called DT-FODPSO technique for automatic segmentation of the structure of the stroke lesion. The approach was validated on 192 MRI images obtained from different stroke subjects. Statistical and morphological features were extracted and classified according to the Oxfordshire community stroke project (OCSP) using support vector machine (SVM) and random forest (RF) classifiers. The method effectively detected the stroke lesions and achieved promising results with an average sensitivity of 0.93, accuracy of 0.95, JI of 0.89 and Dice similarity index of 0.93 using RF classifier. These promising results indicates the DT based optimized approach is efficient in detecting ischemic stroke and it can aid the neuro-radiologists to validate their routine screening.
Collapse
|
23
|
Peixoto SA, Filho PPR, Arun Kumar N, de Albuquerque VHC. Automatic classification of pulmonary diseases using a structural co-occurrence matrix. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3736-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
24
|
Rebouças Filho PP, Peixoto SA, Medeiros da Nóbrega RV, Hemanth DJ, Medeiros AG, Sangaiah AK, de Albuquerque VHC. Automatic histologically-closer classification of skin lesions. Comput Med Imaging Graph 2018; 68:40-54. [DOI: 10.1016/j.compmedimag.2018.05.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 05/20/2018] [Accepted: 05/29/2018] [Indexed: 10/14/2022]
|
25
|
Rebouças EDS, Marques RCP, Braga AM, Oliveira SAF, de Albuquerque VHC, Rebouças Filho PP. New level set approach based on Parzen estimation for stroke segmentation in skull CT images. Soft comput 2018. [DOI: 10.1007/s00500-018-3491-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
26
|
A New Approach to Diagnose Parkinson's Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2018; 2018:7613282. [PMID: 29853835 PMCID: PMC5941776 DOI: 10.1155/2018/7613282] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2018] [Accepted: 03/14/2018] [Indexed: 12/02/2022]
Abstract
Parkinson's disease affects millions of people around the world and consequently various approaches have emerged to help diagnose this disease, among which we can highlight handwriting exams. Extracting features from handwriting exams is an important contribution of the computational field for the diagnosis of this disease. In this paper, we propose an approach that measures the similarity between the exam template and the handwritten trace of the patient following the exam template. This similarity was measured using the Structural Cooccurrence Matrix to calculate how close the handwritten trace of the patient is to the exam template. The proposed approach was evaluated using various exam templates and the handwritten traces of the patient. Each of these variations was used together with the Naïve Bayes, OPF, and SVM classifiers. In conclusion the proposed approach was proven to be better than the existing methods found in the literature and is therefore a promising tool for the diagnosis of Parkinson's disease.
Collapse
|
27
|
Lo CM, Syed-Abdul S, Jack Li YC. The integration of image processing and machine learning for the diagnosis of stroke in CT. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 148:A1. [PMID: 28774444 DOI: 10.1016/s0169-2607(17)30952-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan;; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan;; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taiwan
| | - Yu-Chuan Jack Li
- Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan;; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taiwan;; Chair, Dept. of Dermatology, Wan Fang Hospital, Taipei, Taiwan.
| |
Collapse
|