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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024020759 |
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| Summary: | 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. |
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| ISSN: | 2590-1230 |