Dual-Stream Deep Learning Framework for Brain CT Image Classification and Implications for Polymer Composite Neuro Implant Evaluation

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]15/09/2025 at 12:59 PM[/if 2224] | [if 1553 equals=””] Volume : 13 [else] Volume : [/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 06 | Page :

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    Hari Krishnan G, Umashankar G, Sheeba Santhosh, Ram Prasad Reddy M, Venkata Prasanth,

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  1. Associate Professor, Assistant Professor, Associate Professor, Professor, Professor, Department of Electrical and Electronics Engineering, School of Engineering, Mohan Babu University, Tirupati, Department of Biomedical Engineering, GRT Institute of Engineering and Technology, Tiruvallur, Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, Department of Electrical and Electronics Engineering, Aditya College of Engineering, Madanapalle, Department of Electrical and Electronics Engineering, QIS College of Engineering & Technology, Ongole, Andhra Pradesh, Tamil Nadu, Tamil Nadu, Andhra Pradesh, Andhra Pradesh, India, India, India, India, India

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Abstract

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nEarly and accurate classification of brain CT images is critical for diagnosing conditions such as aneurysms, tumors, and related lesions. We present a dual-stream image-classification framework that fuses convolutional neural network (CNN) features with handcrafted Histogram of Oriented Gradients (HOG) descriptors to jointly capture global semantics and local textural cues. The pipeline begins with modality unification via pixel-wise averaging to form a fused input, which is then processed in parallel by the CNN and HOG branches. Their outputs are concatenated at the feature level and passed to a compact classifier. Training uses standard cross-entropy with Adam and early stopping, and performance is reported using accuracy, precision, recall, F1-score, and confusion matrix analysis. Evaluated on 270 grayscale CT scans, the proposed model delivers uniformly high precision, recall, and F1 across aneurysm, cancer, and tumor classes (0.991–0.995), reflecting balanced detection and discrimination among visually similar pathologies; the confusion matrix shows strong class separation. Beyond neurodiagnostics, the same dual-stream fusion strategy is pertinent to imaging-based assessment of polymer composite neuroimplants (e.g., cranial plates and scaffolds): the CNN pathway encodes global structural context while HOG emphasizes boundary/texture signatures of material interfaces, supporting postoperative integrity checks and positioning verification. Overall, this hybrid CNN–HOG approach advances efficient, accurate CT-based classification while establishing a practical foundation for non-invasive evaluation of polymer composite biomedical implants, thereby bridging clinical imaging and materials engineering within a single automated framework.nn

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Keywords: Brain CT Image Classification, Hybrid CNN–HOG Model, Feature‑Level Fusion, Polymer Composite Neuro‑Implants, Biomedical Imaging Applications, Deep Learning in Neurodiagnostic.

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How to cite this article:
nHari Krishnan G, Umashankar G, Sheeba Santhosh, Ram Prasad Reddy M, Venkata Prasanth. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Dual-Stream Deep Learning Framework for Brain CT Image Classification and Implications for Polymer Composite Neuro Implant Evaluation[/if 2584]. Journal of Polymer and Composites. 15/09/2025; 13(06):-.

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nHari Krishnan G, Umashankar G, Sheeba Santhosh, Ram Prasad Reddy M, Venkata Prasanth. [if 2584 equals=”][226 striphtml=1][else]Dual-Stream Deep Learning Framework for Brain CT Image Classification and Implications for Polymer Composite Neuro Implant Evaluation[/if 2584]. Journal of Polymer and Composites. 15/09/2025; 13(06):-. Available from: https://journals.stmjournals.com/jopc/article=15/09/2025/view=0

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Journal of Polymer and Composites

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Volume 13
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 06
Received 26/08/2025
Accepted 05/09/2025
Published 15/09/2025
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Publication Time 20 Days

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