IRO Journals
Journal of Innovative Image Processing

Quantum Laplacian Pyramid Segmentation for Multi-Class Mushroom Classification

Open Access
Published: 15 July, 2026
Pages: 955-977
  • Jeyapriya N. , Jeyapriya N.
    Research Scholar, Department of Computer Science, Periyar University, Salem, India
    Research Scholar, Department of Computer Science, Periyar University, Salem, India
  • Hannah Inbarani H. , Hannah Inbarani H.
    Professor, Department of Computer Science, Periyar University, Salem, India
    Professor, Department of Computer Science, Periyar University, Salem, India
  • Jamaludeen A. Jamaludeen A.
    Research Scholar, Department of Computer Science, Periyar University, Salem, India
    Research Scholar, Department of Computer Science, Periyar University, Salem, India
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How to Cite

N., Jeyapriya, Hannah Inbarani H., and Jamaludeen A. 2026. “Quantum Laplacian Pyramid Segmentation for Multi-Class Mushroom Classification”. Journal of Innovative Image Processing 8 (3): 955-77. https://doi.org/10.36548/jiip.2026.3.011.
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Keywords

Image classification
EfficientNetB0
Laplacian Residual
Image Segmentation
Quantum Entanglement

Abstract

Quantum computing proposes a new paradigm for image analysis by efficiently representing features in high-dimensional Hilbert spaces. In the first stage, we propose a novel Quantum Laplacian Pyramid Segmentation (QLPS) module to segment complex structural mushroom profiles by exploiting a parameter-efficient, single-layer, 4-qubit variational quantum circuit (VQC) with cyclic ring topology. The second stage combines this quantum-refined mask with a raw high-resolution image track using an element-wise Hadamard product to create a background-suppressed region of interest (ROI) that allows a downstream EfficientNetB0 network to focus entirely on clean biological textures. Rigorous experimental evaluations confirm the system objectives, both individually and collectively. We observe that the QLPS module correlates well with the generated ground truth masks, with a mean Pixel Accuracy (mPA) of 93.79% and a Dice coefficient of 94.26%. The integrated classifier achieves a final classification accuracy of 95.88% across four different categories. Most importantly, comparative ablation testing quantifies the direct architectural contribution of the quantum segmentation step to the classification improvement. The removal of background clutter through the QLPS branch produces a significant 9.56% classification improvement relative to standard classical networks trained on raw, noisy images.

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