Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi
Facial palsy (FP) is a neurological disorder that affects the facial nerve, specifically the seventh nerve, resulting in the patient losing control of the facial muscles on one side of the face. It is an annoying condition that can occur in both children and adults, regardless of gender. Diagnosis b...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/3/2/31 |
| _version_ | 1797595976061419520 |
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| author | Ali Saber Amsalam Ali Al-Naji Ammar Yahya Daeef Javaan Chahl |
| author_facet | Ali Saber Amsalam Ali Al-Naji Ammar Yahya Daeef Javaan Chahl |
| author_sort | Ali Saber Amsalam |
| collection | DOAJ |
| description | Facial palsy (FP) is a neurological disorder that affects the facial nerve, specifically the seventh nerve, resulting in the patient losing control of the facial muscles on one side of the face. It is an annoying condition that can occur in both children and adults, regardless of gender. Diagnosis by visual examination, based on differences in the sides of the face, can be prone to errors and inaccuracies. The detection of FP using artificial intelligence through computer vision systems has become increasingly important. Deep learning is the best solution for detecting FP in real-time with high accuracy, saving patients time, effort, and cost. Therefore, this work proposes a real-time detection system for FP, and for determining the patient’s gender and age, using a Raspberry Pi device with a digital camera and a deep learning algorithm. The solution facilitates the diagnosis process for both the doctor and the patient, and it could be part of a medical assessment activity. This study used a dataset of 20,600 images, containing 19,000 normal images and 1600 FP images, to achieve an accuracy of 98%. Thus, the proposed system is a highly accurate and capable medical diagnostic tool for detecting FP. |
| first_indexed | 2024-03-11T02:43:59Z |
| format | Article |
| id | doaj.art-a15c74c67131431393feb81cc978c7a2 |
| institution | Directory Open Access Journal |
| issn | 2673-7426 |
| language | English |
| last_indexed | 2024-03-11T02:43:59Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj.art-a15c74c67131431393feb81cc978c7a22023-11-18T09:28:09ZengMDPI AGBioMedInformatics2673-74262023-06-013245546610.3390/biomedinformatics3020031Automatic Facial Palsy, Age and Gender Detection Using a Raspberry PiAli Saber Amsalam0Ali Al-Naji1Ammar Yahya Daeef2Javaan Chahl3Electrical Engineering Technical College, Middle Technical University, Baghdad 10022, IraqElectrical Engineering Technical College, Middle Technical University, Baghdad 10022, IraqTechnical Institute for Administration, Middle Technical University, Baghdad 10074, IraqSchool of Engineering, University of South Australia, Adelaide, SA 5000, AustraliaFacial palsy (FP) is a neurological disorder that affects the facial nerve, specifically the seventh nerve, resulting in the patient losing control of the facial muscles on one side of the face. It is an annoying condition that can occur in both children and adults, regardless of gender. Diagnosis by visual examination, based on differences in the sides of the face, can be prone to errors and inaccuracies. The detection of FP using artificial intelligence through computer vision systems has become increasingly important. Deep learning is the best solution for detecting FP in real-time with high accuracy, saving patients time, effort, and cost. Therefore, this work proposes a real-time detection system for FP, and for determining the patient’s gender and age, using a Raspberry Pi device with a digital camera and a deep learning algorithm. The solution facilitates the diagnosis process for both the doctor and the patient, and it could be part of a medical assessment activity. This study used a dataset of 20,600 images, containing 19,000 normal images and 1600 FP images, to achieve an accuracy of 98%. Thus, the proposed system is a highly accurate and capable medical diagnostic tool for detecting FP.https://www.mdpi.com/2673-7426/3/2/31facial palsyraspberry Pireal-timeface detectioncomputer vision systemartificial intelligence |
| spellingShingle | Ali Saber Amsalam Ali Al-Naji Ammar Yahya Daeef Javaan Chahl Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi BioMedInformatics facial palsy raspberry Pi real-time face detection computer vision system artificial intelligence |
| title | Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi |
| title_full | Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi |
| title_fullStr | Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi |
| title_full_unstemmed | Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi |
| title_short | Automatic Facial Palsy, Age and Gender Detection Using a Raspberry Pi |
| title_sort | automatic facial palsy age and gender detection using a raspberry pi |
| topic | facial palsy raspberry Pi real-time face detection computer vision system artificial intelligence |
| url | https://www.mdpi.com/2673-7426/3/2/31 |
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