Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks Article Swipe
Ahmed Mateen Buttar
,
Zein Shaheen
,
Abdu Gumaei
,
Mogeeb A. A. Mosleh
,
Indrajeet Gupta
,
Samah M. Alzanin
,
Muhammad Azeem Akbar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2024.1504545
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2024.1504545
Introduction Neurodegenerative diseases, including Parkinson’s, Alzheimer’s, and epilepsy, pose significant diagnostic and treatment challenges due to their complexity and the gradual degeneration of central nervous system structures. This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability. Methods We propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data. Our approach incorporates key preprocessing techniques, such as reducing noise and normalizing image intensity in MRI scans, alongside an optimized model architecture. The model employs Rectified Linear Unit (ReLU) activation functions, the Adam optimizer, and a random search strategy to fine-tune hyper-parameters like learning rate, batch size, and the number of neurons in fully connected layers. To ensure reliability and broad applicability, cross-fold validation was used. Results and discussion Our DCNN achieved a remarkable classification accuracy of 98.44%, surpassing well-known models such as ResNet-50 and AlexNet when evaluated on a comprehensive MRI dataset. Moreover, performance metrics such as precision, recall, and F1-score were calculated separately, confirming the robustness and efficiency of our model across various evaluation criteria. Statistical analyses, including ANOVA and t-tests, further validated the significance of the performance improvements observed with our proposed method. This model represents an important step toward creating a fully automated system for diagnosing and planning treatment for neurological diseases. The high accuracy of our framework highlights its potential to improve diagnostic workflows by enabling precise detection, tracking disease progression, and supporting personalized treatment strategies. While the results are promising, further research is necessary to assess how the model performs across different clinical scenarios. Future studies could focus on integrating additional data types, such as longitudinal imaging and multimodal techniques, to further enhance diagnostic accuracy and clinical utility. These findings mark a significant advancement in applying deep learning to neuro-diagnostics, with promising implications for improving patient outcomes and clinical practices.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmed.2024.1504545
- https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1504545/pdf
- OA Status
- gold
- Cited By
- 6
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405852274
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405852274Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmed.2024.1504545Digital Object Identifier
- Title
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Enhanced neurological anomaly detection in MRI images using deep convolutional neural networksWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-27Full publication date if available
- Authors
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Ahmed Mateen Buttar, Zein Shaheen, Abdu Gumaei, Mogeeb A. A. Mosleh, Indrajeet Gupta, Samah M. Alzanin, Muhammad Azeem AkbarList of authors in order
- Landing page
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https://doi.org/10.3389/fmed.2024.1504545Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1504545/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1504545/pdfDirect OA link when available
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Computer science, Artificial intelligence, Convolutional neural network, Preprocessor, Deep learning, Pattern recognition (psychology), Robustness (evolution), Machine learning, Gene, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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