ArticleFrontiers in oncology2022
A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 55 citations in OpenAlex.
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- Performance Evaluation of the Deep Learning Based Convolutional Neural Network Approach for the Recognition of Chest X-Ray Images.Frontiers in oncology · 2022Article
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- Ensemble deep learning for brain tumor detection.Frontiers in computational neuroscience · 2022Article
- Segmentation and Classification of Encephalon Tumor by Applying Improved Fast and Robust FCM Algorithm with PSO-Based ELM Technique.Computational intelligence and neuroscience · 2022Article
- Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets.Computational intelligence and neuroscience · 2022Article
- Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process.Computational intelligence and neuroscience · 2022Review
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Authors and funding
8 authors at 6 institutions in 5 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Magnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the tumorous region. Therefore, reliable and automatic segmentation and prediction are necessary for the segmentation of brain tumors. This paper proposes a reliable and efficient neural network variant, i.e., an attention-based convolutional neural network for brain tumor segmentation. Specifically, an encoder part of the UNET is a pre-trained VGG19 network followed by the adjacent decoder parts with an attention gate for segmentation noise induction and a denoising mechanism for avoiding overfitting. The dataset we are using for segmentation is BRATS'20, which comprises four different MRI modalities and one target mask file. The abovementioned algorithm resulted in a dice similarity coefficient of 0.83, 0.86, and 0.90 for enhancing, core, and whole tumors, respectively.
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