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@article{208496,
        author = {Sayali Nale and Tejashree Jena and Anita Atiwadkar},
        title = {A Hybrid Quantum-Classical Deep Learning Framework for Efficient Brain Tumor Classification Using MRI Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {297-301},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208496},
        abstract = {Accurate and computationally efficient classification of brain tumors from MRI scans is critical for deployment in resource-constrained clinical settings, where conventional deep CNN architectures—despite achieving strong accuracy—often require large parameter counts that limit scalability. This work proposes a hybrid quantum-classical deep learning framework that combines a classical convolutional feature extractor with a variational quantum circuit (VQC) for multi-class tumor classification. Spatial features extracted by the CNN backbone will be dimensionality-reduced and encoded into quantum states using angle encoding; a parameterized quantum circuit will then process these features in a higher-dimensional Hilbert space, leveraging superposition and entanglement to capture non-linear feature correlations. Measurement outputs from the quantum layer will be passed to a classical fully connected layer for classification into glioma, meningioma, pituitary tumor, and no-tumor categories. The proposed framework will be evaluated on a publicly available benchmark MRI dataset and benchmarked against state-of-the-art classical CNN architectures in terms of accuracy, precision, recall, F1-score, and parameter efficiency. This study aims to demonstrate that quantum-enhanced layers can achieve competitive classification performance while substantially reducing trainable parameters, offering a practical direction for efficient, quantum-assisted medical image analysis.},
        keywords = {Quantum Machine Learning; Variational Quantum Circuit; Convolutional Neural Network; Brain Tumor Classification; MRI Image Analysis; Hybrid Deep Learning},
        month = {September},
        }