Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical Meningioma
This study aimed to implement an artificial intelligence (AI) model capable of diagnosing meningioma and atypical meningioma during deep learning using magnetic resonance imaging (MRI). The experimental method was to acquire MRI scans of meningiomas and atypical meningiomas using the T2 weighted ima...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/3/188 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849342657712619520 |
|---|---|
| author | Jun-Ho Hwang Seung Hoon Lim Chang Kyu Park |
| author_facet | Jun-Ho Hwang Seung Hoon Lim Chang Kyu Park |
| author_sort | Jun-Ho Hwang |
| collection | DOAJ |
| description | This study aimed to implement an artificial intelligence (AI) model capable of diagnosing meningioma and atypical meningioma during deep learning using magnetic resonance imaging (MRI). The experimental method was to acquire MRI scans of meningiomas and atypical meningiomas using the T2 weighted imaging (T2WI), T1 weighted imaging (T1WI), contrast enhanced T1WI (CE-T1WI), and contrast enhanced fluid attenuated inversion recovery (CE-FLAIR) methods. The MRI results, according to each method, were categorized into two classes for diagnosing either meningioma or atypical meningioma. The CE-FLAIR images tended to have lower learning performance compared to other methods, but all methods showed excellent diagnostic performance. We confirmed that deep learning is a useful method for diagnosing meningioma and atypical meningioma. When using MRI, if the accuracy and loss rate are improved by applying deep learning optimized for medical images, it will be possible to implement a brain tumor diagnosis model with better learning performance. |
| format | Article |
| id | doaj-art-13840d2416634ee2a03af2ae9a875e4a |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-13840d2416634ee2a03af2ae9a875e4a2025-08-20T03:43:16ZengMDPI AGInformation2078-24892025-02-0116318810.3390/info16030188Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical MeningiomaJun-Ho Hwang0Seung Hoon Lim1Chang Kyu Park2Department of Neurosurgery, Kyung Hee University Medical Center, Seoul 02447, Republic of KoreaDepartment of Neurosurgery, Kyung Hee University Medical Center, Seoul 02447, Republic of KoreaDepartment of Neurosurgery, Kyung Hee University Medical Center, Seoul 02447, Republic of KoreaThis study aimed to implement an artificial intelligence (AI) model capable of diagnosing meningioma and atypical meningioma during deep learning using magnetic resonance imaging (MRI). The experimental method was to acquire MRI scans of meningiomas and atypical meningiomas using the T2 weighted imaging (T2WI), T1 weighted imaging (T1WI), contrast enhanced T1WI (CE-T1WI), and contrast enhanced fluid attenuated inversion recovery (CE-FLAIR) methods. The MRI results, according to each method, were categorized into two classes for diagnosing either meningioma or atypical meningioma. The CE-FLAIR images tended to have lower learning performance compared to other methods, but all methods showed excellent diagnostic performance. We confirmed that deep learning is a useful method for diagnosing meningioma and atypical meningioma. When using MRI, if the accuracy and loss rate are improved by applying deep learning optimized for medical images, it will be possible to implement a brain tumor diagnosis model with better learning performance.https://www.mdpi.com/2078-2489/16/3/188artificial intelligence (AI)deep learningmagnetic resonance imaging (MRI)meningiomaatypical meningioma |
| spellingShingle | Jun-Ho Hwang Seung Hoon Lim Chang Kyu Park Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical Meningioma Information artificial intelligence (AI) deep learning magnetic resonance imaging (MRI) meningioma atypical meningioma |
| title | Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical Meningioma |
| title_full | Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical Meningioma |
| title_fullStr | Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical Meningioma |
| title_full_unstemmed | Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical Meningioma |
| title_short | Usefulness of Deep Learning Techniques Using Magnetic Resonance Imaging for the Diagnosis of Meningioma and Atypical Meningioma |
| title_sort | usefulness of deep learning techniques using magnetic resonance imaging for the diagnosis of meningioma and atypical meningioma |
| topic | artificial intelligence (AI) deep learning magnetic resonance imaging (MRI) meningioma atypical meningioma |
| url | https://www.mdpi.com/2078-2489/16/3/188 |
| work_keys_str_mv | AT junhohwang usefulnessofdeeplearningtechniquesusingmagneticresonanceimagingforthediagnosisofmeningiomaandatypicalmeningioma AT seunghoonlim usefulnessofdeeplearningtechniquesusingmagneticresonanceimagingforthediagnosisofmeningiomaandatypicalmeningioma AT changkyupark usefulnessofdeeplearningtechniquesusingmagneticresonanceimagingforthediagnosisofmeningiomaandatypicalmeningioma |