Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images
Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for autom...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/6/3808 |
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| author | Adham Aleid Khalid Alhussaini Reem Alanazi Meaad Altwaimi Omar Altwijri Ali S. Saad |
| author_facet | Adham Aleid Khalid Alhussaini Reem Alanazi Meaad Altwaimi Omar Altwijri Ali S. Saad |
| author_sort | Adham Aleid |
| collection | DOAJ |
| description | Artificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for automatic diagnosis. Diagnostic radiology is evolving from a subjective perceptual talent to a more objective science thanks to AI. Automatic object detection in medical images is an essential AI technology in medicine. The problem of detecting brain tumors at an early stage is well advanced with convolutional neural network (CNN) and deep learning algorithms (DLA). The problem is that those algorithms require a training phase with a big database of more than 500 images and time-consuming with a complex computational and expensive infrastructure. This study proposes a classical automatic segmentation method for detecting brain tumors in the early stage using MRI images. It is based on a multilevel thresholding technique on a harmony search algorithm (HSO); the algorithm was developed to suit MRI brain segmentation, and parameters selection was optimized for the purpose. Multiple thresholds, based on the variance and entropy functions, break the histogram into multiple portions, and different colors are associated with each portion. To eliminate the tiny arias supposed as noise and detect brain tumors, morphological operations followed by a connected component analysis are utilized after segmentation. The brain tumor detection performance is judged using performance parameters such as Accuracy, Dice Coefficient, and Jaccard index. The results are compared to those acquired manually by experts in the field. The results were further compared with different CNN and DLA approaches using Brain Images dataset called the “BraTS 2017 challenge”. The average Dice Index was used as a performance measure for the comparison. The results of the proposed approach were found to be competitive in accuracy to those obtained by CNN and DLA methods and much better in terms of execution time, computational complexity, and data management. |
| first_indexed | 2024-03-11T06:59:07Z |
| format | Article |
| id | doaj.art-fbf9f117bc134c6ea9bf2f1cd7dffbfa |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:59:07Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fbf9f117bc134c6ea9bf2f1cd7dffbfa2023-11-17T09:27:09ZengMDPI AGApplied Sciences2076-34172023-03-01136380810.3390/app13063808Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI ImagesAdham Aleid0Khalid Alhussaini1Reem Alanazi2Meaad Altwaimi3Omar Altwijri4Ali S. Saad5Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh 11433, Saudi ArabiaDepartment of Biomedical Technology, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh 11433, Saudi ArabiaDepartment of Biomedical Technology, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh 11433, Saudi ArabiaDepartment of Biomedical Technology, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh 11433, Saudi ArabiaDepartment of Biomedical Technology, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh 11433, Saudi ArabiaDepartment of Biomedical Technology, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh 11433, Saudi ArabiaArtificial intelligence (AI) is one of the most promising approaches to health innovation. The use of AI in image recognition considerably extends findings beyond the constraints of human sight. The application of AI in medical imaging, which relies on picture interpretation, is beneficial for automatic diagnosis. Diagnostic radiology is evolving from a subjective perceptual talent to a more objective science thanks to AI. Automatic object detection in medical images is an essential AI technology in medicine. The problem of detecting brain tumors at an early stage is well advanced with convolutional neural network (CNN) and deep learning algorithms (DLA). The problem is that those algorithms require a training phase with a big database of more than 500 images and time-consuming with a complex computational and expensive infrastructure. This study proposes a classical automatic segmentation method for detecting brain tumors in the early stage using MRI images. It is based on a multilevel thresholding technique on a harmony search algorithm (HSO); the algorithm was developed to suit MRI brain segmentation, and parameters selection was optimized for the purpose. Multiple thresholds, based on the variance and entropy functions, break the histogram into multiple portions, and different colors are associated with each portion. To eliminate the tiny arias supposed as noise and detect brain tumors, morphological operations followed by a connected component analysis are utilized after segmentation. The brain tumor detection performance is judged using performance parameters such as Accuracy, Dice Coefficient, and Jaccard index. The results are compared to those acquired manually by experts in the field. The results were further compared with different CNN and DLA approaches using Brain Images dataset called the “BraTS 2017 challenge”. The average Dice Index was used as a performance measure for the comparison. The results of the proposed approach were found to be competitive in accuracy to those obtained by CNN and DLA methods and much better in terms of execution time, computational complexity, and data management.https://www.mdpi.com/2076-3417/13/6/3808artificial intelligencesegmentationbrain tumorMRI imagingimage processing |
| spellingShingle | Adham Aleid Khalid Alhussaini Reem Alanazi Meaad Altwaimi Omar Altwijri Ali S. Saad Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images Applied Sciences artificial intelligence segmentation brain tumor MRI imaging image processing |
| title | Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images |
| title_full | Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images |
| title_fullStr | Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images |
| title_full_unstemmed | Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images |
| title_short | Artificial Intelligence Approach for Early Detection of Brain Tumors Using MRI Images |
| title_sort | artificial intelligence approach for early detection of brain tumors using mri images |
| topic | artificial intelligence segmentation brain tumor MRI imaging image processing |
| url | https://www.mdpi.com/2076-3417/13/6/3808 |
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