Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning
Brain tumors pose a significant threat to human health due to their potential to disrupt normal brain function. Early and accurate detection is crucial for effective treatment. This study proposes a novel triple-module approach for automated brain tumor classification from MRI images. The first modu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024020759 |
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| author | Yugal Pande Jyotismita Chaki |
| author_facet | Yugal Pande Jyotismita Chaki |
| author_sort | Yugal Pande |
| collection | DOAJ |
| description | Brain tumors pose a significant threat to human health due to their potential to disrupt normal brain function. Early and accurate detection is crucial for effective treatment. This study proposes a novel triple-module approach for automated brain tumor classification from MRI images. The first module utilizes pre-trained deep learning models (DenseNet121 and ResNet101) to extract informative features. The second module employs Principal Component Analysis (PCA) for dimensionality reduction. Finally, the third module utilizes a Random Forest classifier for tumor classification. The proposed model is evaluated on multiple datasets, demonstrating impressive performance (accuracy is >90 %) on both high-quality and noisy images. The results highlight the potential of this approach for improving brain tumor diagnosis and treatment planning. |
| format | Article |
| id | doaj-art-1efaa30e1b93412789cef22c0253d885 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-1efaa30e1b93412789cef22c0253d8852025-01-04T04:56:56ZengElsevierResults in Engineering2590-12302025-03-0125103832Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learningYugal Pande0Jyotismita Chaki1School of Electronics Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India; Corresponding author.Brain tumors pose a significant threat to human health due to their potential to disrupt normal brain function. Early and accurate detection is crucial for effective treatment. This study proposes a novel triple-module approach for automated brain tumor classification from MRI images. The first module utilizes pre-trained deep learning models (DenseNet121 and ResNet101) to extract informative features. The second module employs Principal Component Analysis (PCA) for dimensionality reduction. Finally, the third module utilizes a Random Forest classifier for tumor classification. The proposed model is evaluated on multiple datasets, demonstrating impressive performance (accuracy is >90 %) on both high-quality and noisy images. The results highlight the potential of this approach for improving brain tumor diagnosis and treatment planning.http://www.sciencedirect.com/science/article/pii/S2590123024020759Brain tumor classificationDeep learningMachine learningMRI image analysisFeature extractionDimensionality reduction |
| spellingShingle | Yugal Pande Jyotismita Chaki Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning Results in Engineering Brain tumor classification Deep learning Machine learning MRI image analysis Feature extraction Dimensionality reduction |
| title | Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning |
| title_full | Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning |
| title_fullStr | Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning |
| title_full_unstemmed | Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning |
| title_short | Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning |
| title_sort | brain tumor detection across diverse mr images an automated triple module approach integrating reduced fused deep features and machine learning |
| topic | Brain tumor classification Deep learning Machine learning MRI image analysis Feature extraction Dimensionality reduction |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024020759 |
| work_keys_str_mv | AT yugalpande braintumordetectionacrossdiversemrimagesanautomatedtriplemoduleapproachintegratingreducedfuseddeepfeaturesandmachinelearning AT jyotismitachaki braintumordetectionacrossdiversemrimagesanautomatedtriplemoduleapproachintegratingreducedfuseddeepfeaturesandmachinelearning |