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Robust cascade bidirectional triple capsule network with OOA for deep neural network‐based improved brain tumor identification.

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Title: Robust cascade bidirectional triple capsule network with OOA for deep neural network‐based improved brain tumor identification.
Authors: Thangavel, Kavitha1 (AUTHOR) kavisasi2009@gmail.com, Murugavelu, Mathivanan2 (AUTHOR), Nelson, Selvin Christalin3 (AUTHOR), Sreekanth, Gobichettipalayam Ramakrishnan4 (AUTHOR)
Source: Medical Physics. Mar2026, Vol. 53 Issue 3, p1-15. 15p.
Subjects: Capsule neural networks, Metaheuristic algorithms, Diagnostic imaging, Magnetic resonance imaging, Cancer diagnosis, Feature extraction, Deep learning, Artificial neural networks
Abstract: Background: Abnormal growth of brain cells may produce serious neurological symptoms (migraines, seizures, and cognitive impairments), which are called brain tumors. Early and precise diagnosis is of utmost importance in enhancing prognosis and treatment options such as surgery, radiation, or chemotherapy. Although the current systems have improved in Deep Learning (DL), they continue to record poor accuracy and high false positive rates, which warrant more trustworthy solutions. Purpose: In this paper, the authors propose a Robust Triple Extraction with Cascade Bidirectional Capsule Network and Osprey Optimization Algorithm (RT‐CBCN‐OOA) to achieve a higher quality, accuracy, and dependability of brain tumor detection and classification. Methods: The pre‐processing of the BraTS and Figshare MRI images is performed with the help of the Modified Square Root Sage‐Husa Adaptive Kalman Filter (MSRS‐HAKF) in order to eliminate noise and enhance image clarity. Dual‐Domain Attention CNN based on EfficientNet‐B3 CNN (EN‐B3 CNN‐2DA) is used to extract features and segment and classify these features using the Geometric Algebra Transformer‐based Robust Cascade Bidirectional Triple Capsule Network with Triple Attention (GAT‐RCBTCN‐TA). The Osprey Optimization Algorithm (OOA) optimizes the performance of model weights. Results and conclusion: The proposed RT‐CBCN‐OOA has a recall and accuracy of 99.9 and 99.8, respectively, which is better than the current models. It provides a powerful, precise, and efficient brain tumor detection, which proves to have a great future in clinical use in medical imaging and diagnosis. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: Robust cascade bidirectional triple capsule network with OOA for deep neural network‐based improved brain tumor identification.
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  Data: <searchLink fieldCode="AR" term="%22Thangavel%2C+Kavitha%22">Thangavel, Kavitha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> kavisasi2009@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Murugavelu%2C+Mathivanan%22">Murugavelu, Mathivanan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nelson%2C+Selvin+Christalin%22">Nelson, Selvin Christalin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sreekanth%2C+Gobichettipalayam+Ramakrishnan%22">Sreekanth, Gobichettipalayam Ramakrishnan</searchLink><relatesTo>4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Mar2026, Vol. 53 Issue 3, p1-15. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Capsule+neural+networks%22">Capsule neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+diagnosis%22">Cancer diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
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  Label: Abstract
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  Data: Background: Abnormal growth of brain cells may produce serious neurological symptoms (migraines, seizures, and cognitive impairments), which are called brain tumors. Early and precise diagnosis is of utmost importance in enhancing prognosis and treatment options such as surgery, radiation, or chemotherapy. Although the current systems have improved in Deep Learning (DL), they continue to record poor accuracy and high false positive rates, which warrant more trustworthy solutions. Purpose: In this paper, the authors propose a Robust Triple Extraction with Cascade Bidirectional Capsule Network and Osprey Optimization Algorithm (RT‐CBCN‐OOA) to achieve a higher quality, accuracy, and dependability of brain tumor detection and classification. Methods: The pre‐processing of the BraTS and Figshare MRI images is performed with the help of the Modified Square Root Sage‐Husa Adaptive Kalman Filter (MSRS‐HAKF) in order to eliminate noise and enhance image clarity. Dual‐Domain Attention CNN based on EfficientNet‐B3 CNN (EN‐B3 CNN‐2DA) is used to extract features and segment and classify these features using the Geometric Algebra Transformer‐based Robust Cascade Bidirectional Triple Capsule Network with Triple Attention (GAT‐RCBTCN‐TA). The Osprey Optimization Algorithm (OOA) optimizes the performance of model weights. Results and conclusion: The proposed RT‐CBCN‐OOA has a recall and accuracy of 99.9 and 99.8, respectively, which is better than the current models. It provides a powerful, precise, and efficient brain tumor detection, which proves to have a great future in clinical use in medical imaging and diagnosis. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mp.70375
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        Text: English
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        PageCount: 15
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    Subjects:
      – SubjectFull: Capsule neural networks
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Diagnostic imaging
        Type: general
      – SubjectFull: Magnetic resonance imaging
        Type: general
      – SubjectFull: Cancer diagnosis
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
    Titles:
      – TitleFull: Robust cascade bidirectional triple capsule network with OOA for deep neural network‐based improved brain tumor identification.
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            NameFull: Thangavel, Kavitha
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            NameFull: Nelson, Selvin Christalin
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            NameFull: Sreekanth, Gobichettipalayam Ramakrishnan
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              Text: Mar2026
              Type: published
              Y: 2026
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              Value: 53
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