Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural netwo...

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Published in:Scientific reports 2023-09, Vol.13 (1), p.14522-14522, Article 14522
Main Authors: Prakash, B. V., Kannan, A. Rajiv, Santhiyakumari, N., Kumarganesh, S., Raja, D. Siva Sundhara, Hephzipah, J. Jasmine, MartinSagayam, K., Pomplun, Marc, Dang, Hien
Format: Article
Language:English
Subjects:
631/114
639/705
Accuracy
Algorithms
Brain cancer
Brain tumors
Classification
Datasets
Humanities and Social Sciences
Image processing
Magnetic resonance imaging
Meningioma
multidisciplinary
Neural networks
Neuroimaging
Science
Science (multidisciplinary)
Segmentation
Tumors
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Summary:The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images. The HCNN classification technique consists of the Ridgelet transform, feature computations, classifier module, and segmentation algorithm. Pixel stability during the decomposition process was improved by the Ridgelet transform, and the features were computed from the coefficient of the Ridgelet. These features were classified using the HCNN classification approach, and tumor pixels were detected using the segmentation algorithm. The experimental results were analyzed for meningioma tumor images by applying the proposed method to the BRATS 2019 and Nanfang dataset. The proposed HCNN-based meningioma detection system achieved 99.31% sensitivity, 99.37% specificity, and 99.24% segmentation accuracy for the BRATS 2019 dataset. The proposed HCNN technique achieved99.35% sensitivity, 99.22% specificity, and 99.04% segmentation accuracy on brain Magnetic Resonance Imaging (MRI) in the Nanfang dataset. The proposed system obtains 99.81% classification accuracy, 99.2% sensitivity, 99.7% specificity and 99.8% segmentation accuracy on BRATS 2022 dataset. The experimental results of the proposed HCNN algorithm were compared with those of the state-of-the-art meningioma detection algorithms in this study.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-41576-6