Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor Article Swipe
Atika Akter
,
Nazeela Nosheen
,
Sabbir Ahmed
,
Mariom Hossain
,
Mohammad Abu Yousuf
,
Mohammad Ali Abdullah Almoyad
,
Khondokar Fida Hasan
,
Mohammad Ali Moni
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.eswa.2023.122347
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.eswa.2023.122347
Early diagnosis of brain tumors is critical for enhancing patient prognosis and treatment options, while accurate classification and segmentation of brain tumors are vital for developing personalized treatment strategies. Despite the widespread use of Magnetic Resonance Imaging (MRI) for brain examination and advances in AI-based detection methods, building an accurate and efficient model for detecting and categorizing tumors from MRI images remains a challenge. To address this problem, we proposed a deep Convolutional Neural Network (CNN)-based architecture for automatic brain image classification into four classes and a U-Net-based segmentation model. Using six benchmarked datasets, we tested the classification model and trained the segmentation model, enabling side-by-side comparison of the impact of segmentation on tumor classification in brain MRI images. We also evaluated two classification methods based on accuracy, recall, precision, and AUC. Our developed novel deep learning-based model for brain tumor classification and segmentation outperforms existing pre-trained models across all six datasets. The results demonstrate that our classification model achieved the highest accuracy of 98.7% in a merged dataset and 98.8% with the segmentation approach, with the highest classification accuracy reaching 97.7% among the four individual datasets. Thus, this novel framework could be applicable in clinics for the automatic identification and segmentation of brain tumors utilizing MRI scan input images.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.eswa.2023.122347
- OA Status
- hybrid
- Cited By
- 188
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388190812
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388190812Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.eswa.2023.122347Digital Object Identifier
- Title
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Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumorWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-28Full publication date if available
- Authors
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Atika Akter, Nazeela Nosheen, Sabbir Ahmed, Mariom Hossain, Mohammad Abu Yousuf, Mohammad Ali Abdullah Almoyad, Khondokar Fida Hasan, Mohammad Ali MoniList of authors in order
- Landing page
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https://doi.org/10.1016/j.eswa.2023.122347Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.eswa.2023.122347Direct OA link when available
- Concepts
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Segmentation, Computer science, Artificial intelligence, Convolutional neural network, Pattern recognition (psychology), Deep learning, Brain tumor, Image segmentation, Magnetic resonance imaging, Artificial neural network, Machine learning, Radiology, Medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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188Total citation count in OpenAlex
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2025: 102, 2024: 86Per-year citation counts (last 5 years)
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63Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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