An intelligent gradient-guided hybrid inpainting framework for brain MRI reconstruction and Alzheimer's disease classification in connected healthcare systems.

Overview

1. School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
2. Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India
3. Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
4. Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India
5. Research Fellow, INTI International University, Nilai, Negeri Sembilan, Malaysia

Abstract

Brain magnetic resonance imaging (MRI) is essential for early Alzheimer's disease diagnosis, yet clinical scans are often degraded by motion artifacts, signal loss, or incomplete acquisitions. Image inpainting offers a promising preprocessing solution, but existing methods have limitations: deep learning models such as LaMa generate visually plausible reconstructions but may compromise structural fidelity, while classical diffusion-based approaches like OpenCV Telea preserve local continuity but tend to oversmooth complex anatomy. This study proposes a gradient-guided hybrid inpainting framework that integrates OpenCV Telea and LaMa to leverage their complementary strengths. A gradient magnitude-based weighting mechanism enables structure-aware reconstruction, assigning edge-rich regions to the classical method and smoother regions to the deep model, thereby preserving both fine anatomical details and global consistency. Experiments on a four-class ADNI-based MRI dataset, balanced using SMOTE–NM and split 70:15:15, with 10%–30% random masking, demonstrate improved reconstruction performance. The proposed method reduces mean squared error by 8% compared to LaMa and 30% compared to OpenCV, while achieving SSIM ≈0.93 and PSNR ≈25.7 dB. A VGG16 classifier trained on clean images achieves 94.35% accuracy on hybrid-inpainted data, showing only a 1.69 percentage point drop from the baseline and outperforming individual methods. These results highlight the effectiveness of the proposed framework for reliable, AI-driven neuroimaging pipelines in intelligent and connected healthcare systems.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• kaggle.com/datasets/drsaeedmohsen, at Kaggle; found in “Data availability statement”

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: the original data presented in the study are openly available in [Kaggle Repository] at https://www.kaggle.com/datasets/drsaeedmohsen/alzheimer-dataset (accessed on 12 Dec 2025).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 8 keywords, 27 references.

Cite

This paper

Yadav, C., Yadav, S., Panwar, A., Donelli, M., & Jain, A. (2026). An intelligent gradient-guided hybrid inpainting framework for brain MRI reconstruction and Alzheimer's disease classification in connected healthcare systems. Frontiers in medicine, 13, 1849122. https://doi.org/10.3389/fmed.2026.1849122

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