Brain Tumor Detection Using a Deep CNN Model
The diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field of medical science. Relying solely on MR imaging for the detection and categorization of brain tumors demands significant time, effort, and expertise from medical professionals. This und...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/7634426 |
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| author | Sonia Ben Brahim Samia Dardouri Ridha Bouallegue |
| author_facet | Sonia Ben Brahim Samia Dardouri Ridha Bouallegue |
| author_sort | Sonia Ben Brahim |
| collection | DOAJ |
| description | The diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field of medical science. Relying solely on MR imaging for the detection and categorization of brain tumors demands significant time, effort, and expertise from medical professionals. This underscores the need for an autonomous model for brain tumor diagnosis. Our study involves the application of a deep convolutional neural network (DCNN) to diagnose brain tumors from MR images. The application of these algorithms offers several benefits, including rapid brain tumor prediction, reduced errors, and enhanced precision. The proposed model is built upon the state-of-the-art CNN architecture VGG16, employing a data augmentation approach. The dataset utilized in this paper consists of 3000 brain MR images sourced from Kaggle, with 1500 images reported to contain tumors. Through training and testing, the pretrained CNN model achieves a precision and classification accuracy rate of 96%, and the loss is 1%. Moreover, it achieves an average precision, recall, and F1-score of 98.7%, 97.44%, and 98.06%, respectively. These evaluation metric values demonstrate the effectiveness of the proposed solution. |
| format | Article |
| id | doaj-art-af13d479fde34feab215905ce05f0afe |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-af13d479fde34feab215905ce05f0afe2025-02-03T11:38:23ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/7634426Brain Tumor Detection Using a Deep CNN ModelSonia Ben Brahim0Samia Dardouri1Ridha Bouallegue2InnoV’COM Laboratory-Sup’ComInnoV’COM Laboratory-Sup’ComInnoV’COM Laboratory-Sup’ComThe diagnosis of brain tumors through magnetic resonance imaging (MRI) has become highly significant in the field of medical science. Relying solely on MR imaging for the detection and categorization of brain tumors demands significant time, effort, and expertise from medical professionals. This underscores the need for an autonomous model for brain tumor diagnosis. Our study involves the application of a deep convolutional neural network (DCNN) to diagnose brain tumors from MR images. The application of these algorithms offers several benefits, including rapid brain tumor prediction, reduced errors, and enhanced precision. The proposed model is built upon the state-of-the-art CNN architecture VGG16, employing a data augmentation approach. The dataset utilized in this paper consists of 3000 brain MR images sourced from Kaggle, with 1500 images reported to contain tumors. Through training and testing, the pretrained CNN model achieves a precision and classification accuracy rate of 96%, and the loss is 1%. Moreover, it achieves an average precision, recall, and F1-score of 98.7%, 97.44%, and 98.06%, respectively. These evaluation metric values demonstrate the effectiveness of the proposed solution.http://dx.doi.org/10.1155/2024/7634426 |
| spellingShingle | Sonia Ben Brahim Samia Dardouri Ridha Bouallegue Brain Tumor Detection Using a Deep CNN Model Applied Computational Intelligence and Soft Computing |
| title | Brain Tumor Detection Using a Deep CNN Model |
| title_full | Brain Tumor Detection Using a Deep CNN Model |
| title_fullStr | Brain Tumor Detection Using a Deep CNN Model |
| title_full_unstemmed | Brain Tumor Detection Using a Deep CNN Model |
| title_short | Brain Tumor Detection Using a Deep CNN Model |
| title_sort | brain tumor detection using a deep cnn model |
| url | http://dx.doi.org/10.1155/2024/7634426 |
| work_keys_str_mv | AT soniabenbrahim braintumordetectionusingadeepcnnmodel AT samiadardouri braintumordetectionusingadeepcnnmodel AT ridhabouallegue braintumordetectionusingadeepcnnmodel |