ArticleJournal of cancer prevention2022
A Deep Learning Architecture for Meningioma Brain Tumor Detection and Segmentation.
Article in Journal of cancer prevention, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.Neuroinformatics · 2025Pooled it
- Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
The meningioma brain tumor detection and segmentation method is a complex process due to its low intensity pixel profile. In this article, the meningioma brain tumor images were detected and tumor regions were segmented using a convolutional neural network (CNN) classification approach. The source brain MRI images were decomposed using the discrete wavelet transform and these decomposed sub bands were fused using an arithmetic fusion technique. The fused image was data augmented in order to increase the sample size. The data augmented images were classified into either healthy or malignant using a CNN classifier. Then, the tumor region in the classified meningioma brain image was segmented using an connection component analysis algorithm. The tumor region segmented meningioma brain image was compressed using a lossless compression technique. The proposed method stated in this article was experimentally tested with the sets of meningioma brain images from an open access dataset. The experimental results were compared with existing methods in terms of sensitivity, specificity and tumor segmentation accuracy.
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Registered trials
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