A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain Tumors
The brain is regarded as one of the most effective body-controlling organs. The development of technology has enabled the early and accurate detection of brain tumors, which makes a significant difference in their treatment. The adoption of AI has grown substantially in the arena of neurology. This...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Russian Academy of Sciences, St. Petersburg Federal Research Center
2023-05-01
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| Series: | Информатика и автоматизация |
| Subjects: | |
| Online Access: | http://ia.spcras.ru/index.php/sp/article/view/15692 |
| _version_ | 1797821823675531264 |
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| author | Sanjeet Kumar Urmila Pilania Neha Nandal |
| author_facet | Sanjeet Kumar Urmila Pilania Neha Nandal |
| author_sort | Sanjeet Kumar |
| collection | DOAJ |
| description | The brain is regarded as one of the most effective body-controlling organs. The development of technology has enabled the early and accurate detection of brain tumors, which makes a significant difference in their treatment. The adoption of AI has grown substantially in the arena of neurology. This systematic review compares recent Deep Learning (DL), Machine Learning (ML), and hybrid methods for detecting brain cancers. This article evaluates 36 recent articles on these techniques, considering datasets, methodology, tools used, merits, and limitations. The articles contain comprehensible graphs and tables. The detection of brain tumors relies heavily on ML techniques such as Support Vector Machines (SVM) and Fuzzy C-Means (FCM). Recurrent Convolutional Neural Networks (RCNN), DenseNet, Convolutional Neural Networks (CNN), ResNet, and Deep Neural Networks (DNN) are DL techniques used to detect brain tumors more efficiently. DL and ML techniques are merged to develop hybrid techniques. In addition, a summary of the various image processing steps is provided. The systematic review identifies outstanding issues and future goals for DL and ML-based techniques for detecting brain tumors. Through a systematic review, the most effective method for detecting brain tumors can be identified and utilized for improvement. |
| first_indexed | 2024-03-13T09:58:30Z |
| format | Article |
| id | doaj.art-46f5f581ece348ff975768c004cc491b |
| institution | Directory Open Access Journal |
| issn | 2713-3192 2713-3206 |
| language | English |
| last_indexed | 2024-03-13T09:58:30Z |
| publishDate | 2023-05-01 |
| publisher | Russian Academy of Sciences, St. Petersburg Federal Research Center |
| record_format | Article |
| series | Информатика и автоматизация |
| spelling | doaj.art-46f5f581ece348ff975768c004cc491b2023-05-23T09:21:54ZengRussian Academy of Sciences, St. Petersburg Federal Research CenterИнформатика и автоматизация2713-31922713-32062023-05-0122354157510.15622/ia.22.3.315692A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain TumorsSanjeet Kumar0Urmila Pilania1Neha Nandal2University of DelhiManav Rachna UniversityGokaraju Rangaraju Institute of Engineering and TechnologyThe brain is regarded as one of the most effective body-controlling organs. The development of technology has enabled the early and accurate detection of brain tumors, which makes a significant difference in their treatment. The adoption of AI has grown substantially in the arena of neurology. This systematic review compares recent Deep Learning (DL), Machine Learning (ML), and hybrid methods for detecting brain cancers. This article evaluates 36 recent articles on these techniques, considering datasets, methodology, tools used, merits, and limitations. The articles contain comprehensible graphs and tables. The detection of brain tumors relies heavily on ML techniques such as Support Vector Machines (SVM) and Fuzzy C-Means (FCM). Recurrent Convolutional Neural Networks (RCNN), DenseNet, Convolutional Neural Networks (CNN), ResNet, and Deep Neural Networks (DNN) are DL techniques used to detect brain tumors more efficiently. DL and ML techniques are merged to develop hybrid techniques. In addition, a summary of the various image processing steps is provided. The systematic review identifies outstanding issues and future goals for DL and ML-based techniques for detecting brain tumors. Through a systematic review, the most effective method for detecting brain tumors can be identified and utilized for improvement.http://ia.spcras.ru/index.php/sp/article/view/15692image processingmachine learningdeep learninghybrid techniques |
| spellingShingle | Sanjeet Kumar Urmila Pilania Neha Nandal A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain Tumors Информатика и автоматизация image processing machine learning deep learning hybrid techniques |
| title | A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain Tumors |
| title_full | A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain Tumors |
| title_fullStr | A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain Tumors |
| title_full_unstemmed | A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain Tumors |
| title_short | A Systematic Study of Artificial Intelligence-Based Methods for Detecting Brain Tumors |
| title_sort | systematic study of artificial intelligence based methods for detecting brain tumors |
| topic | image processing machine learning deep learning hybrid techniques |
| url | http://ia.spcras.ru/index.php/sp/article/view/15692 |
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