Enhanced MRI-based brain tumor segmentation and feature extraction using Berkeley wavelet transform and ETCCNN
Objective Brain tumors are abnormal growths of brain cells that are typically diagnosed via magnetic resonance imaging (MRI), which helps to discriminate between malignant and benign tumors. Using MRI image analysis, tumor sites have been identified and classified into four distinct tumor categories...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2024-12-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241305282 |
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| _version_ | 1846117417756917760 |
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| author | Dilip Kumar Gokapay Sachi Nandan Mohanty |
| author_facet | Dilip Kumar Gokapay Sachi Nandan Mohanty |
| author_sort | Dilip Kumar Gokapay |
| collection | DOAJ |
| description | Objective Brain tumors are abnormal growths of brain cells that are typically diagnosed via magnetic resonance imaging (MRI), which helps to discriminate between malignant and benign tumors. Using MRI image analysis, tumor sites have been identified and classified into four distinct tumor categories: meningioma, glioma, not tumor, and pituitary. If a brain tumor is not detected in its early stages, it could progress to a severe level or cause death. Therefore, to address these issues, the proposed approach uses an efficient classifier based on deep learning for brain tumor detection. Methods This article describes the classification and detection of brain tumor by an efficient two-channel convolutional neural network. The input image is initially rotated during the augmentation stage. Morphological operations, thresholding, and region filling are then used in the pre-processing stage. The output is then segmented using the Berkeley Wavelet Transform. A two-channel convolutional neural network is used to extract features from segmented objects. In the end, the most effective deep neural network is employed to determine the features of brain tumors. The classifier will utilize the Enhanced Serval Optimization Algorithm to determine the optimal gain parameters. MATLAB serves as the platform of choice for implementing the suggested model. Results Several performance metrics are calculated to assess the proposed brain tumor detection method, such as accuracy, F measures, kappa, precision, sensitivity, and specificity. The proposed model has a 98.8% detection accuracy for brain tumors. Conclusion The evaluation shows that the suggested strategy has produced the best results. |
| format | Article |
| id | doaj-art-778f347e7fdc4ebd813b68bcad6b6776 |
| institution | Kabale University |
| issn | 2055-2076 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-778f347e7fdc4ebd813b68bcad6b67762024-12-18T11:04:03ZengSAGE PublishingDigital Health2055-20762024-12-011010.1177/20552076241305282Enhanced MRI-based brain tumor segmentation and feature extraction using Berkeley wavelet transform and ETCCNNDilip Kumar GokapaySachi Nandan MohantyObjective Brain tumors are abnormal growths of brain cells that are typically diagnosed via magnetic resonance imaging (MRI), which helps to discriminate between malignant and benign tumors. Using MRI image analysis, tumor sites have been identified and classified into four distinct tumor categories: meningioma, glioma, not tumor, and pituitary. If a brain tumor is not detected in its early stages, it could progress to a severe level or cause death. Therefore, to address these issues, the proposed approach uses an efficient classifier based on deep learning for brain tumor detection. Methods This article describes the classification and detection of brain tumor by an efficient two-channel convolutional neural network. The input image is initially rotated during the augmentation stage. Morphological operations, thresholding, and region filling are then used in the pre-processing stage. The output is then segmented using the Berkeley Wavelet Transform. A two-channel convolutional neural network is used to extract features from segmented objects. In the end, the most effective deep neural network is employed to determine the features of brain tumors. The classifier will utilize the Enhanced Serval Optimization Algorithm to determine the optimal gain parameters. MATLAB serves as the platform of choice for implementing the suggested model. Results Several performance metrics are calculated to assess the proposed brain tumor detection method, such as accuracy, F measures, kappa, precision, sensitivity, and specificity. The proposed model has a 98.8% detection accuracy for brain tumors. Conclusion The evaluation shows that the suggested strategy has produced the best results.https://doi.org/10.1177/20552076241305282 |
| spellingShingle | Dilip Kumar Gokapay Sachi Nandan Mohanty Enhanced MRI-based brain tumor segmentation and feature extraction using Berkeley wavelet transform and ETCCNN Digital Health |
| title | Enhanced MRI-based brain tumor segmentation and feature extraction using Berkeley wavelet transform and ETCCNN |
| title_full | Enhanced MRI-based brain tumor segmentation and feature extraction using Berkeley wavelet transform and ETCCNN |
| title_fullStr | Enhanced MRI-based brain tumor segmentation and feature extraction using Berkeley wavelet transform and ETCCNN |
| title_full_unstemmed | Enhanced MRI-based brain tumor segmentation and feature extraction using Berkeley wavelet transform and ETCCNN |
| title_short | Enhanced MRI-based brain tumor segmentation and feature extraction using Berkeley wavelet transform and ETCCNN |
| title_sort | enhanced mri based brain tumor segmentation and feature extraction using berkeley wavelet transform and etccnn |
| url | https://doi.org/10.1177/20552076241305282 |
| work_keys_str_mv | AT dilipkumargokapay enhancedmribasedbraintumorsegmentationandfeatureextractionusingberkeleywavelettransformandetccnn AT sachinandanmohanty enhancedmribasedbraintumorsegmentationandfeatureextractionusingberkeleywavelettransformandetccnn |