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

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

  • By

  • Chhaya Yadav

  • Sunita Yadav

  • Arvind Panwar

  • Massimo Donelli

  • Achin Jain

  • June 9, 2026

  • 0 min

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  • 1

    The study introduces a gradient-guided hybrid inpainting framework combining OpenCV Telea and LaMa for improved brain MRI reconstruction.

  • 2

    This hybrid approach achieves an 8% reduction in mean squared error compared to LaMa and 30% compared to OpenCV, enhancing image quality.

  • 3

    The method maintains a peak signal-to-noise ratio of approximately 25.7 dB and a structural similarity index of about 0.93 across various conditions.

  • 4

    A VGG16 classifier trained on clean images achieves 94.35% accuracy on hybrid-inpainted data, showing minimal accuracy loss compared to original images.

  • 5

    The proposed framework demonstrates effectiveness in preserving diagnostically relevant features for Alzheimer's disease classification in MRI data.

Original Source(s)

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

  • by Chhaya Yadav, Sunita Yadav, Arvind Panwar, Massimo Donelli, Achin Jain

  • June 9, 2026

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