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@article{191653,
        author = {Jeyapriya N and Hannah Inbarani H and Jamaludeen A},
        title = {Quantum-Optimized Deep Feature Framework for Multi-Class Mushroom Image Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7291-7296},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191653},
        abstract = {Accurate differentiation of mushroom species is critically important for both biological applications and safety-critical contexts, where misclassification can lead to significant negative consequences. Conventional identification techniques often face limitations due to the striking visual similarities among numerous species. To address this, automated image-driven categorization systems have emerged as a vital area of research. This study leverages quantum-driven learning paradigms, which operate within classical computational frameworks by utilizing probabilistic representations inspired by quantum theory, to efficiently capture intricate feature relationships and enhance classification outcomes. We propose a novel framework that integrates a quantum mechanics-inspired, neighborhood-based classifier with deep visual feature learning via MobileNetV2. To further improve classification reliability, an automatic parameter adjustment approach based on quantum-behaved particle swarm optimization is incorporated. Experimental results demonstrate a high classification accuracy of 98.75%, highlighting the framework's effectiveness and scalability for multi-class mushroom image analysis.},
        keywords = {Image Classification, Feature Extraction, MobileNetV2, Quantum K-NN, QPSO.},
        month = {January},
        }