Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging
Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/5/1960 |
| _version_ | 1797473711124643840 |
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| author | Mahsa Arabahmadi Reza Farahbakhsh Javad Rezazadeh |
| author_facet | Mahsa Arabahmadi Reza Farahbakhsh Javad Rezazadeh |
| author_sort | Mahsa Arabahmadi |
| collection | DOAJ |
| description | Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on “braintumor” website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images. |
| first_indexed | 2024-03-09T20:20:21Z |
| format | Article |
| id | doaj.art-536abec25ecd4874af4b91f8cfd63d36 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T20:20:21Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-536abec25ecd4874af4b91f8cfd63d362023-11-23T23:49:07ZengMDPI AGSensors1424-82202022-03-01225196010.3390/s22051960Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical ImagingMahsa Arabahmadi0Reza Farahbakhsh1Javad Rezazadeh2North Tehran Branch, Azad University, Tehran 1667914161, IranInstitut Polytechnique de Paris, Telecom SudParis, 91000 Evry, FranceNorth Tehran Branch, Azad University, Tehran 1667914161, IranAdvances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on “braintumor” website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images.https://www.mdpi.com/1424-8220/22/5/1960smart healthcarebrain tumor classificationMRIdeep neural networksCNNGAN |
| spellingShingle | Mahsa Arabahmadi Reza Farahbakhsh Javad Rezazadeh Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging Sensors smart healthcare brain tumor classification MRI deep neural networks CNN GAN |
| title | Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging |
| title_full | Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging |
| title_fullStr | Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging |
| title_full_unstemmed | Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging |
| title_short | Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging |
| title_sort | deep learning for smart healthcare a survey on brain tumor detection from medical imaging |
| topic | smart healthcare brain tumor classification MRI deep neural networks CNN GAN |
| url | https://www.mdpi.com/1424-8220/22/5/1960 |
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