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Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra
Parkinson’s disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other ne...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486663/ https://www.ncbi.nlm.nih.gov/pubmed/37685365 http://dx.doi.org/10.3390/diagnostics13172827 |
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author | Hussain, Sayyed Shahid Degang, Xu Shah, Pir Masoom Islam, Saif Ul Alam, Mahmood Khan, Izaz Ahmad Awwad, Fuad A. Ismail, Emad A. A. |
author_facet | Hussain, Sayyed Shahid Degang, Xu Shah, Pir Masoom Islam, Saif Ul Alam, Mahmood Khan, Izaz Ahmad Awwad, Fuad A. Ismail, Emad A. A. |
author_sort | Hussain, Sayyed Shahid |
collection | PubMed |
description | Parkinson’s disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson’s Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network. |
format | Online Article Text |
id | pubmed-10486663 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104866632023-09-09 Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra Hussain, Sayyed Shahid Degang, Xu Shah, Pir Masoom Islam, Saif Ul Alam, Mahmood Khan, Izaz Ahmad Awwad, Fuad A. Ismail, Emad A. A. Diagnostics (Basel) Article Parkinson’s disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson’s Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network. MDPI 2023-08-31 /pmc/articles/PMC10486663/ /pubmed/37685365 http://dx.doi.org/10.3390/diagnostics13172827 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hussain, Sayyed Shahid Degang, Xu Shah, Pir Masoom Islam, Saif Ul Alam, Mahmood Khan, Izaz Ahmad Awwad, Fuad A. Ismail, Emad A. A. Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra |
title | Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra |
title_full | Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra |
title_fullStr | Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra |
title_full_unstemmed | Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra |
title_short | Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra |
title_sort | classification of parkinson’s disease in patch-based mri of substantia nigra |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10486663/ https://www.ncbi.nlm.nih.gov/pubmed/37685365 http://dx.doi.org/10.3390/diagnostics13172827 |
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