Automated Brain Tumor Segmentation and Classification Through MRI Images
Communications in Computer and Information Science, ISSN: 1865-0937, Vol: 1548 CCIS, Page: 182-194
2022
- 9Citations
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
The brain tumor is considered a hazardous infection and may cause death. Therefore, early detection of brain tumors can improve the survival rate. This paper presents convolutional neural network method for segmentation and classification of brain tumors using magnetic resonance images. The proposed method has achieved an accuracy of 98.97%, specificity of 97.35%, sensitivity of 97%, precision of 97.90%, and F1-score of 96% for brain tumor segmentation. While on brain tumor classification, the proposed method shows accuracy 98.25%, sensitivity 98%, specificity 98.5%, precision 97.21% and F1-score 97%. The BRATS 2020 dataset has been utilized for training and testing the proposed method for brain tumor segmentation and classification.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85127683100&origin=inward; http://dx.doi.org/10.1007/978-3-030-97255-4_13; https://link.springer.com/10.1007/978-3-030-97255-4_13; https://dx.doi.org/10.1007/978-3-030-97255-4_13; https://link.springer.com/chapter/10.1007/978-3-030-97255-4_13
Springer Science and Business Media LLC
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