Computer Vision System for Facial Palsy Detection Article Swipe
Ali Saber Amsalam
,
Ali Al‐Naji
,
Ammar Yahya Daeef
,
Javaan Chahl
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.51173/jt.v5i1.1133
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.51173/jt.v5i1.1133
Facial palsy (FP) is a disorder that affects the seventh facial nerve, which makes the patient unable to control facial movements and expressions with other vital activities. It affects one side of the face, and it is usually diagnosed by the asymmetry of the two sides of the face through visual inspection by a doctor. However, the visual inspection is human-based, which is prone to errors because the doctor is exposed to omission due to fatigue and work stress. Therefore, it is important to develop a new method for detecting FP through artificial intelligence and use a more accurate computerized system to reduce the effort and cost of patients and increase the accuracy of diagnosis. This work aims to establish a safe, useful and high-accuracy diagnostic system for FP that can be used by the patient and proposes to detect FP using a digital camera and deep learning techniques automatically. The system could be used by the patient himself at home without needing to visit the hospital. The proposed system trained 570 images, including 200 images of FP palsy. The proposed FP system achieved an accuracy of 98%. This confirms the effectiveness of the proposed system and makes it an efficient medical examination tool for detecting FP.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.51173/jt.v5i1.1133
- https://journal.mtu.edu.iq/index.php/MTU/article/download/1133/244
- OA Status
- diamond
- Cited By
- 13
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362731103
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4362731103Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.51173/jt.v5i1.1133Digital Object Identifier
- Title
-
Computer Vision System for Facial Palsy DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-31Full publication date if available
- Authors
-
Ali Saber Amsalam, Ali Al‐Naji, Ammar Yahya Daeef, Javaan ChahlList of authors in order
- Landing page
-
https://doi.org/10.51173/jt.v5i1.1133Publisher landing page
- PDF URL
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https://journal.mtu.edu.iq/index.php/MTU/article/download/1133/244Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://journal.mtu.edu.iq/index.php/MTU/article/download/1133/244Direct OA link when available
- Concepts
-
Palsy, Artificial intelligence, Computer science, Computer vision, Face (sociological concept), Facial expression, Medicine, Pathology, Social science, Sociology, Alternative medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 9, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.two | 44 |
| abstract_inverted_index.use | 95 |
| abstract_inverted_index.(FP) | 2 |
| abstract_inverted_index.98%. | 187 |
| abstract_inverted_index.This | 115, 188 |
| abstract_inverted_index.aims | 117 |
| abstract_inverted_index.cost | 106 |
| abstract_inverted_index.deep | 146 |
| abstract_inverted_index.face | 48 |
| abstract_inverted_index.home | 160 |
| abstract_inverted_index.more | 97 |
| abstract_inverted_index.side | 30 |
| abstract_inverted_index.that | 6, 129 |
| abstract_inverted_index.tool | 203 |
| abstract_inverted_index.used | 132, 154 |
| abstract_inverted_index.with | 23 |
| abstract_inverted_index.work | 77, 116 |
| abstract_inverted_index.could | 152 |
| abstract_inverted_index.face, | 33 |
| abstract_inverted_index.makes | 13, 197 |
| abstract_inverted_index.other | 24 |
| abstract_inverted_index.palsy | 1 |
| abstract_inverted_index.prone | 63 |
| abstract_inverted_index.safe, | 121 |
| abstract_inverted_index.sides | 45 |
| abstract_inverted_index.using | 141 |
| abstract_inverted_index.visit | 164 |
| abstract_inverted_index.vital | 25 |
| abstract_inverted_index.which | 12, 61 |
| abstract_inverted_index.Facial | 0 |
| abstract_inverted_index.camera | 144 |
| abstract_inverted_index.detect | 139 |
| abstract_inverted_index.doctor | 68 |
| abstract_inverted_index.effort | 104 |
| abstract_inverted_index.errors | 65 |
| abstract_inverted_index.facial | 10, 19 |
| abstract_inverted_index.images | 175 |
| abstract_inverted_index.method | 87 |
| abstract_inverted_index.nerve, | 11 |
| abstract_inverted_index.palsy. | 178 |
| abstract_inverted_index.reduce | 102 |
| abstract_inverted_index.system | 100, 126, 151, 169, 182, 195 |
| abstract_inverted_index.unable | 16 |
| abstract_inverted_index.useful | 122 |
| abstract_inverted_index.visual | 50, 57 |
| abstract_inverted_index.affects | 7, 28 |
| abstract_inverted_index.because | 66 |
| abstract_inverted_index.control | 18 |
| abstract_inverted_index.develop | 84 |
| abstract_inverted_index.digital | 143 |
| abstract_inverted_index.doctor. | 54 |
| abstract_inverted_index.exposed | 70 |
| abstract_inverted_index.fatigue | 75 |
| abstract_inverted_index.himself | 158 |
| abstract_inverted_index.images, | 172 |
| abstract_inverted_index.medical | 201 |
| abstract_inverted_index.needing | 162 |
| abstract_inverted_index.patient | 15, 135, 157 |
| abstract_inverted_index.seventh | 9 |
| abstract_inverted_index.stress. | 78 |
| abstract_inverted_index.through | 49, 91 |
| abstract_inverted_index.trained | 170 |
| abstract_inverted_index.usually | 37 |
| abstract_inverted_index.without | 161 |
| abstract_inverted_index.However, | 55 |
| abstract_inverted_index.accuracy | 112, 185 |
| abstract_inverted_index.accurate | 98 |
| abstract_inverted_index.achieved | 183 |
| abstract_inverted_index.confirms | 189 |
| abstract_inverted_index.disorder | 5 |
| abstract_inverted_index.increase | 110 |
| abstract_inverted_index.learning | 147 |
| abstract_inverted_index.omission | 72 |
| abstract_inverted_index.patients | 108 |
| abstract_inverted_index.proposed | 168, 180, 194 |
| abstract_inverted_index.proposes | 137 |
| abstract_inverted_index.asymmetry | 41 |
| abstract_inverted_index.detecting | 89, 205 |
| abstract_inverted_index.diagnosed | 38 |
| abstract_inverted_index.efficient | 200 |
| abstract_inverted_index.establish | 119 |
| abstract_inverted_index.hospital. | 166 |
| abstract_inverted_index.important | 82 |
| abstract_inverted_index.including | 173 |
| abstract_inverted_index.movements | 20 |
| abstract_inverted_index.Therefore, | 79 |
| abstract_inverted_index.artificial | 92 |
| abstract_inverted_index.diagnosis. | 114 |
| abstract_inverted_index.diagnostic | 125 |
| abstract_inverted_index.inspection | 51, 58 |
| abstract_inverted_index.techniques | 148 |
| abstract_inverted_index.activities. | 26 |
| abstract_inverted_index.examination | 202 |
| abstract_inverted_index.expressions | 22 |
| abstract_inverted_index.computerized | 99 |
| abstract_inverted_index.human-based, | 60 |
| abstract_inverted_index.intelligence | 93 |
| abstract_inverted_index.effectiveness | 191 |
| abstract_inverted_index.high-accuracy | 124 |
| abstract_inverted_index.automatically. | 149 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.919896 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |