Enhanced Brain Tumor MRI Classification Using Stationary Wavelet Transform, ResNet50V2, and LSTM Networks
Brain tumors constitute a significant health issue in the world today because of their aggressive behavior and short survival rates. Early and accurate detection of brain tumors is necessary for effective treatment and improved patient outcomes. The principal diagnostic technology that shows highly...
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| Format: | Article |
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Institute of Technology and Education Galileo da Amazônia
2025-01-01
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| Series: | ITEGAM-JETIA |
| Online Access: | http://itegam-jetia.org/journal/index.php/jetia/article/view/1457 |
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| author | Oussama Abda Hilal NAIMI |
| author_facet | Oussama Abda Hilal NAIMI |
| author_sort | Oussama Abda |
| collection | DOAJ |
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Brain tumors constitute a significant health issue in the world today because of their aggressive behavior and short survival rates. Early and accurate detection of brain tumors is necessary for effective treatment and improved patient outcomes. The principal diagnostic technology that shows highly detailed visualization of brain structures is Magnetic Resonance Imaging (MRI); however, the interpretation of these images can be time-consuming and require expertise and highly specialized manpower. This study presents a new approach for brain tumor classification, which combines advanced preprocessing, feature extraction, and classification techniques. The preprocessing includes Stationary Wavelet Transform (SWT) intended to enhance tumor-relevant features and resizing to standard MRI image dimensions; feature extraction includes. After that a Long Short Term Memory network receives the features. that will model the dependencies in the feature space and classifies into four categories: Glioma, Meningioma, Pituitary tumors, and No Tumor. Experiments showed that this proposed method can be effective in producing a high classification accuracy rate along with time quality processing. This work brought forward the prospects of developing an automated, accurate, and reliable brain tumor classification system from SWT, ResNet50V2, and LSTM, whereas otherwise, it catered for needs in the enhancement of diagnostic tools in medical imaging. The method was analyzed using the Kaggle dataset and scored an amazing accuracy of 98.7%, which proved the effectiveness of the method in improving brain tumor classification.
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| format | Article |
| id | doaj-art-20f5db6a2d8540bb833a6d44ca087bee |
| institution | Kabale University |
| issn | 2447-0228 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Institute of Technology and Education Galileo da Amazônia |
| record_format | Article |
| series | ITEGAM-JETIA |
| spelling | doaj-art-20f5db6a2d8540bb833a6d44ca087bee2025-02-06T23:51:54ZengInstitute of Technology and Education Galileo da AmazôniaITEGAM-JETIA2447-02282025-01-01115110.5935/jetia.v11i51.1457Enhanced Brain Tumor MRI Classification Using Stationary Wavelet Transform, ResNet50V2, and LSTM NetworksOussama Abda0Hilal NAIMI1Laboratoire de Recherche Modélisation Simulation et Optimisation des Systèmes ComplexesRéels, University of Djelfa, Djelfa, 17000, Algeria.Laboratoire de Recherche Modélisation Simulation et Optimisation des Systèmes ComplexesRéels, University of Djelfa, Djelfa, 17000, Algeria. Brain tumors constitute a significant health issue in the world today because of their aggressive behavior and short survival rates. Early and accurate detection of brain tumors is necessary for effective treatment and improved patient outcomes. The principal diagnostic technology that shows highly detailed visualization of brain structures is Magnetic Resonance Imaging (MRI); however, the interpretation of these images can be time-consuming and require expertise and highly specialized manpower. This study presents a new approach for brain tumor classification, which combines advanced preprocessing, feature extraction, and classification techniques. The preprocessing includes Stationary Wavelet Transform (SWT) intended to enhance tumor-relevant features and resizing to standard MRI image dimensions; feature extraction includes. After that a Long Short Term Memory network receives the features. that will model the dependencies in the feature space and classifies into four categories: Glioma, Meningioma, Pituitary tumors, and No Tumor. Experiments showed that this proposed method can be effective in producing a high classification accuracy rate along with time quality processing. This work brought forward the prospects of developing an automated, accurate, and reliable brain tumor classification system from SWT, ResNet50V2, and LSTM, whereas otherwise, it catered for needs in the enhancement of diagnostic tools in medical imaging. The method was analyzed using the Kaggle dataset and scored an amazing accuracy of 98.7%, which proved the effectiveness of the method in improving brain tumor classification. http://itegam-jetia.org/journal/index.php/jetia/article/view/1457 |
| spellingShingle | Oussama Abda Hilal NAIMI Enhanced Brain Tumor MRI Classification Using Stationary Wavelet Transform, ResNet50V2, and LSTM Networks ITEGAM-JETIA |
| title | Enhanced Brain Tumor MRI Classification Using Stationary Wavelet Transform, ResNet50V2, and LSTM Networks |
| title_full | Enhanced Brain Tumor MRI Classification Using Stationary Wavelet Transform, ResNet50V2, and LSTM Networks |
| title_fullStr | Enhanced Brain Tumor MRI Classification Using Stationary Wavelet Transform, ResNet50V2, and LSTM Networks |
| title_full_unstemmed | Enhanced Brain Tumor MRI Classification Using Stationary Wavelet Transform, ResNet50V2, and LSTM Networks |
| title_short | Enhanced Brain Tumor MRI Classification Using Stationary Wavelet Transform, ResNet50V2, and LSTM Networks |
| title_sort | enhanced brain tumor mri classification using stationary wavelet transform resnet50v2 and lstm networks |
| url | http://itegam-jetia.org/journal/index.php/jetia/article/view/1457 |
| work_keys_str_mv | AT oussamaabda enhancedbraintumormriclassificationusingstationarywavelettransformresnet50v2andlstmnetworks AT hilalnaimi enhancedbraintumormriclassificationusingstationarywavelettransformresnet50v2andlstmnetworks |