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Iqbal MS, Belal Bin Heyat M, Parveen S, Ammar Bin Hayat M, Roshanzamir M, Alizadehsani R, Akhtar F, Sayeed E, Hussain S, Hussein HS, Sawan M. Progress and trends in neurological disorders research based on deep learning. Comput Med Imaging Graph 2024; 116:102400. [PMID: 38851079 DOI: 10.1016/j.compmedimag.2024.102400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 06/10/2024]
Abstract
In recent years, deep learning (DL) has emerged as a powerful tool in clinical imaging, offering unprecedented opportunities for the diagnosis and treatment of neurological disorders (NDs). This comprehensive review explores the multifaceted role of DL techniques in leveraging vast datasets to advance our understanding of NDs and improve clinical outcomes. Beginning with a systematic literature review, we delve into the utilization of DL, particularly focusing on multimodal neuroimaging data analysis-a domain that has witnessed rapid progress and garnered significant scientific interest. Our study categorizes and critically analyses numerous DL models, including Convolutional Neural Networks (CNNs), LSTM-CNN, GAN, and VGG, to understand their performance across different types of Neurology Diseases. Through particular analysis, we identify key benchmarks and datasets utilized in training and testing DL models, shedding light on the challenges and opportunities in clinical neuroimaging research. Moreover, we discuss the effectiveness of DL in real-world clinical scenarios, emphasizing its potential to revolutionize ND diagnosis and therapy. By synthesizing existing literature and describing future directions, this review not only provides insights into the current state of DL applications in ND analysis but also covers the way for the development of more efficient and accessible DL techniques. Finally, our findings underscore the transformative impact of DL in reshaping the landscape of clinical neuroimaging, offering hope for enhanced patient care and groundbreaking discoveries in the field of neurology. This review paper is beneficial for neuropathologists and new researchers in this field.
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Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University of Azad Jammu & Kashmir, Bagh, Pakistan.
| | - Md Belal Bin Heyat
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
| | - Saba Parveen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China.
| | | | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, VIC 3216, Australia.
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
| | - Eram Sayeed
- Kisan Inter College, Dhaurahara, Kushinagar, India.
| | - Sadiq Hussain
- Department of Examination, Dibrugarh University, Assam 786004, India.
| | - Hany S Hussein
- Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81528, Egypt.
| | - Mohamad Sawan
- CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
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Gorji A, Fathi Jouzdani A. Machine learning for predicting cognitive decline within five years in Parkinson's disease: Comparing cognitive assessment scales with DAT SPECT and clinical biomarkers. PLoS One 2024; 19:e0304355. [PMID: 39018311 PMCID: PMC11253925 DOI: 10.1371/journal.pone.0304355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 05/08/2024] [Indexed: 07/19/2024] Open
Abstract
OBJECTIVE Parkinson's disease (PD) is an age-related neurodegenerative condition characterized mostly by motor symptoms. Although a wide range of non-motor symptoms (NMS) are frequently experienced by PD patients. One of the important and common NMS is cognitive impairment, which is measured using different cognitive scales. Monitoring cognitive impairment and its decline in PD is essential for patient care and management. In this study, our goal is to identify the most effective cognitive scale in predicting cognitive decline over a 5-year timeframe initializing clinical biomarkers and DAT SPECT. METHODS Machine Learning has previously shown superior performance in image and clinical data classification and detection. In this study, we propose to use machine learning with different types of data, such as DAT SPECT and clinical biomarkers, to predict PD-CD based on various cognitive scales. We collected 330 DAT SPECT images and their clinical data in baseline, years 2,3,4, and 5 from Parkinson's Progression Markers Initiative (PPMI). We then designed a 3D Autoencoder to extract deep radiomic features (DF) from DAT SPECT images, and we then concatenated it with 17 clinical features (CF) to predict cognitive decline based on Montreal Cognitive Assessment (MoCA) and The Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-I). RESULTS The utilization of MoCA as a cognitive decline scale yielded better performance in various years compared to MDS-UPDRS-I. In year 4, the application of the deep radiomic feature resulted in the highest achievement, with a cross-validation AUC of 89.28, utilizing the gradient boosting classifier. For the MDS-UPDRS-I scale, the highest achievement was obtained by utilizing the deep radiomic feature, resulting in a cross-validation AUC of 81.34 with the random forest classifier. CONCLUSIONS The study findings indicate that the MoCA scale may be a more effective predictor of cognitive decline within 5 years compared to MDS-UPDRS-I. Furthermore, deep radiomic features had better performance compared to sole clinical biomarkers or clinical and deep radiomic combined. These results suggest that using the MoCA score and deep radiomic features extracted from DAT SPECT could be a promising approach for identifying individuals at risk for cognitive decline in four years. Future research is needed to validate these findings and explore their utility in clinical practice.
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Affiliation(s)
- Arman Gorji
- Department of Neuroscience, School of Science and Advanced Technologies in Medicine, Neuroscience and Artificial Intelligence Research Group (NAIRG), Hamadan University of Medical Sciences, Hamadan, Iran
- USERN Office, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Ali Fathi Jouzdani
- Department of Neuroscience, School of Science and Advanced Technologies in Medicine, Neuroscience and Artificial Intelligence Research Group (NAIRG), Hamadan University of Medical Sciences, Hamadan, Iran
- USERN Office, Hamadan University of Medical Sciences, Hamadan, Iran
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Mani K, Rajaguru H. A framework for performance enhancement of classifiers in detection of prostate cancer from microarray gene. Heliyon 2024; 10:e29630. [PMID: 38720727 PMCID: PMC11076651 DOI: 10.1016/j.heliyon.2024.e29630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/26/2024] [Accepted: 04/11/2024] [Indexed: 05/12/2024] Open
Abstract
Prostate cancer is a major world health problem for men. This shows how important early detection and accurate diagnosis are for better treatment and patient outcomes. This study compares different ways to find Prostate Cancer (PCa) and label tumors as normal or abnormal, with the goal of speeding up current work in microarray gene data analysis. The study looks at how well several feature extraction methods work with three feature selection strategies: Harmonic Search (HS), Firefly Algorithm (FA), and Elephant Herding Optimization (EHO). The techniques tested are Expectation Maximization (EM), Nonlinear Regression (NLR), K-means, Principal Component Analysis (PCA), and Discrete Cosine Transform (DCT). Eight classifiers are used for the task of classification. These are Random Forest, Decision Tree, Adaboost, XGBoost, and Support Vector Machine (SVM) with linear, polynomial, and radial basis function kernels. This study looks at how well these classifiers work with and without feature selection methods. It finds that the SVM with radial basis function kernel, using DCT for feature extraction and EHO for feature selection, does the best of all of them, with an accuracy of 94.8 % and an error rate of 5.15 %.
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Sharma S. Artificial intelligence for fracture diagnosis in orthopedic X-rays: current developments and future potential. SICOT J 2023; 9:21. [PMID: 37409882 PMCID: PMC10324466 DOI: 10.1051/sicotj/2023018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/17/2023] [Indexed: 07/07/2023] Open
Abstract
The use of artificial intelligence (AI) in the interpretation of orthopedic X-rays has shown great potential to improve the accuracy and efficiency of fracture diagnosis. AI algorithms rely on large datasets of annotated images to learn how to accurately classify and diagnose abnormalities. One way to improve AI interpretation of X-rays is to increase the size and quality of the datasets used for training, and to incorporate more advanced machine learning techniques, such as deep reinforcement learning, into the algorithms. Another approach is to integrate AI algorithms with other imaging modalities, such as computed tomography (CT) scans, and magnetic resonance imaging (MRI), to provide a more comprehensive and accurate diagnosis. Recent studies have shown that AI algorithms can accurately detect and classify fractures of the wrist and long bones on X-ray images, demonstrating the potential of AI to improve the accuracy and efficiency of fracture diagnosis. These findings suggest that AI has the potential to significantly improve patient outcomes in the field of orthopedics.
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Affiliation(s)
- Sanskrati Sharma
- Department of Orthopedics, Royal Preston Hospital Sharoe Green Ln, Fulwood Preston PR2 9HT United Kingdom
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Qamar MA, Rota S, Batzu L, Subramanian I, Falup-Pecurariu C, Titova N, Metta V, Murasan L, Odin P, Padmakumar C, Kukkle PL, Borgohain R, Kandadai RM, Goyal V, Chaudhuri KR. Chaudhuri's Dashboard of Vitals in Parkinson's syndrome: an unmet need underpinned by real life clinical tests. Front Neurol 2023; 14:1174698. [PMID: 37305739 PMCID: PMC10248458 DOI: 10.3389/fneur.2023.1174698] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/02/2023] [Indexed: 06/13/2023] Open
Abstract
We have recently published the notion of the "vitals" of Parkinson's, a conglomeration of signs and symptoms, largely nonmotor, that must not be missed and yet often not considered in neurological consultations, with considerable societal and personal detrimental consequences. This "dashboard," termed the Chaudhuri's vitals of Parkinson's, are summarized as 5 key vital symptoms or signs and comprise of (a) motor, (b) nonmotor, (c) visual, gut, and oral health, (d) bone health and falls, and finally (e) comorbidities, comedication, and dopamine agonist side effects, such as impulse control disorders. Additionally, not addressing the vitals also may reflect inadequate management strategies, leading to worsening quality of life and diminished wellness, a new concept for people with Parkinson's. In this paper, we discuss possible, simple to use, and clinically relevant tests that can be used to monitor the status of these vitals, so that these can be incorporated into clinical practice. We also use the term Parkinson's syndrome to describe Parkinson's disease, as the term "disease" is now abandoned in many countries, such as the U.K., reflecting the heterogeneity of Parkinson's, which is now considered by many as a syndrome.
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Affiliation(s)
- Mubasher A. Qamar
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Silvia Rota
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Lucia Batzu
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Indu Subramanian
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
- Parkinson’s Disease Research, Education and Clinical Centers, Greater Los Angeles Veterans Affairs Medical Center, Los Angeles, CA, United States
| | - Cristian Falup-Pecurariu
- Faculty of Medicine, Transilvania University of Braşov, Brașov, Romania
- Department of Neurology, County Clinic Hospital, Brașov, Romania
| | - Nataliya Titova
- Department of Neurology, Neurosurgery and Medical Genetics, Federal State Autonomous Educational Institution of Higher Education “N.I. Pirogov Russian National Research Medical University” of the Ministry of Health of the Russian Federation, Moscow, Russia
- Department of Neurodegenerative Diseases, Federal State Budgetary Institution “Federal Center of Brain Research and Neurotechnologies” of the Federal Medical Biological Agency, Moscow, Russia
| | - Vinod Metta
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
| | - Lulia Murasan
- Faculty of Medicine, Transilvania University of Braşov, Brașov, Romania
- Department of Neurology, County Clinic Hospital, Brașov, Romania
| | - Per Odin
- Department of Neurology, University Hospital, Lund, Sweden
| | | | - Prashanth L. Kukkle
- Center for Parkinson’s Disease and Movement Disorders, Manipal Hospital, Karnataka, India, Bangalore
- Parkinson’s Disease and Movement Disorders Clinic, Bangalore, Karnataka, India
| | - Rupam Borgohain
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Rukmini Mridula Kandadai
- Department of Neurology, Nizam’s Institute of Medical Sciences, Autonomous University, Hyderabad, India
| | - Vinay Goyal
- Neurology Department, Medanta, Gurugram, India
| | - Kallo Ray Chaudhuri
- Institute of Psychiatry, Psychology and Neuroscience, Department of Basic and Clinical Neuroscience, Division of Neuroscience, King’s College London, London, United Kingdom
- King’s College Hospital NHS Foundation Trust, London, United Kingdom
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Gerraty RT, Provost A, Li L, Wagner E, Haas M, Lancashire L. Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs. Front Aging Neurosci 2023; 15:1076657. [PMID: 36861121 PMCID: PMC9968811 DOI: 10.3389/fnagi.2023.1076657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/16/2023] [Indexed: 02/17/2023] Open
Abstract
The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.
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Affiliation(s)
| | | | - Lin Li
- PharmaLex, Frederick, MD, United States
| | | | - Magali Haas
- Cohen Veterans Bioscience, New York, NY, United States
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Chaki J, Woźniak M. Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Rana A, Dumka A, Singh R, Panda MK, Priyadarshi N. A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives. Diagnostics (Basel) 2022; 12:2708. [PMID: 36359550 PMCID: PMC9689408 DOI: 10.3390/diagnostics12112708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 08/03/2023] Open
Abstract
According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.
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Affiliation(s)
- Arti Rana
- Computer Science & Engineering, Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India
| | - Ankur Dumka
- Department of Computer Science and Engineering, Women Institute of Technology, Dehradun 248007, Uttarakhand, India
- Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun 248001, Uttarakhand, India
| | - Rajesh Singh
- Division of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
| | - Manoj Kumar Panda
- Department of Electrical Engineering, G.B. Pant Institute of Engineering and Technology, Pauri 246194, Uttarakhand, India
| | - Neeraj Priyadarshi
- Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, West Bengal, India
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9
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Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data. MATHEMATICS 2022. [DOI: 10.3390/math10152566] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
In this paper, we used the single-photon emission computerized tomography (SPECT) imaging technique to visualize the deficiency of dopamine-generated patterns inside the brain. These patterns are used to establish a patient’s disease progression, which helps distinguish the patients into different categories. Furthermore, we used a convolutional neural network (CNN) model to classify the patients based on the dopamine level inside the brain. The dataset used throughout this paper is the Parkinson’s progressive markers initiative (PPMI) dataset. The collected dataset was pre-processed and data amplification was performed to balance the imbalanced dataset. A CNN-based neural network was defined to classify input SPECT images into four categories. The motivation behind the proposed model is to reduce the number of resources consumed while maintaining the performance of the classification model. This will help the healthcare ecosystem run the classification model on mobile devices. The proposed model contains 14 layers with input layers, convolutional layers, max-pool layers, flatten layers, and dense layers with different dimensions. The dense layer classifies the patients into four different categories, including PSD, healthy control, scans without evidence of dopaminergic deficit (SWEDD), and GenReg PSD from the entire SPECT imaging dataset, which is used to establish the disease progression of different patients using SPECT images. The proposed model is trained with a large dataset with 58,692 images for training and 11,738 images for validation, and 7826 for testing. The proposed model outperforms the classification models from the surveyed papers. The proposed model’s accuracy is 0.889, recall is 0.9012, the precision is 0.9104, and the F1-score is 0.9057.
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Lei H, Zhang Y, Li H, Huang Z, Liu CH, Zhou F, Tan EL, Xiao X, Lei Y, Hu H, Huang Y, Lei B. Gene-related Parkinson's disease diagnosis via feature-based multi-branch octave convolution network. Comput Biol Med 2022; 148:105859. [DOI: 10.1016/j.compbiomed.2022.105859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 06/14/2022] [Accepted: 06/21/2022] [Indexed: 11/25/2022]
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Kurmi A, Biswas S, Sen S, Sinitca A, Kaplun D, Sarkar R. An Ensemble of CNN Models for Parkinson’s Disease Detection Using DaTscan Images. Diagnostics (Basel) 2022; 12:diagnostics12051173. [PMID: 35626328 PMCID: PMC9139649 DOI: 10.3390/diagnostics12051173] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 04/09/2022] [Accepted: 05/04/2022] [Indexed: 12/04/2022] Open
Abstract
Parkinson’s Disease (PD) is a progressive central nervous system disorder that is caused due to the neural degeneration mainly in the substantia nigra in the brain. It is responsible for the decline of various motor functions due to the loss of dopamine-producing neurons. Tremors in hands is usually the initial symptom, followed by rigidity, bradykinesia, postural instability, and impaired balance. Proper diagnosis and preventive treatment can help patients improve their quality of life. We have proposed an ensemble of Deep Learning (DL) models to predict Parkinson’s using DaTscan images. Initially, we have used four DL models, namely, VGG16, ResNet50, Inception-V3, and Xception, to classify Parkinson’s disease. In the next stage, we have applied a Fuzzy Fusion logic-based ensemble approach to enhance the overall result of the classification model. The proposed model is assessed on a publicly available database provided by the Parkinson’s Progression Markers Initiative (PPMI). The achieved recognition accuracy, Precision, Sensitivity, Specificity, F1-score from the proposed model are 98.45%, 98.84%, 98.84%, 97.67%, and 98.84%, respectively which are higher than the individual model. We have also developed a Graphical User Interface (GUI)-based software tool for public use that instantly detects all classes using Magnetic Resonance Imaging (MRI) with reasonable accuracy. The proposed method offers better performance compared to other state-of-the-art methods in detecting PD. The developed GUI-based software tool can play a significant role in detecting the disease in real-time.
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Affiliation(s)
- Ankit Kurmi
- Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani 741235, West Bengal, India;
| | - Shreya Biswas
- Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, West Bengal, India;
| | - Shibaprasad Sen
- Department of Computer Science and Technology, University of Engineering and Management, Kolkata 700160, West Bengal, India;
| | - Aleksandr Sinitca
- Research Centre for Digital Telecommunication Technologies, Saint Petersburg Electrotechnical University ”LETI”, 197022 St. Petersburg, Russia;
| | - Dmitrii Kaplun
- Department of Automation and Control Processes, Saint Petersburg Electrotechnical University ”LETI”, 197022 St. Petersburg, Russia;
| | - Ram Sarkar
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, West Bengal, India
- Correspondence:
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Parkinson’s disease diagnosis using neural networks: Survey and comprehensive evaluation. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102909] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Bai J, Sun B, Chu X, Wang T, Li H, Huang Q. Neighborhood rough set-based multi-attribute prediction approach and its application of gout patients. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108127] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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