A quantum computing approach for brain tumor image classification using amplitude encoding technique

Brain tumor image classification is an essential pre-processing task for tumor diagnosis and treatment planning. In medical imaging, quantum computing analyse medical imaging data, offering potential speedups and new representations compared to classical methods. The brain images are transformed through resizing and normalization to a uniform 32 × 32 × 3 resolution. Subsequently, these processed images are converted into vectors to enable encoding into quantum states. The feature set is reduced using Principal Component Analysis (PCA) to ensure compatibility with the restricted qubit count in the quantum circuits. Through amplitude encoding, these vectors are mapped to quantum states, allowing for a compact and efficient data representation within the quantum domain. A parameterized Variational Quantum Circuit(VQC), consisting of entangling gates and trainable rotations, processes the encoded quantum states to learn features that can effectively discriminate between classes. The loss is calculated from the measured expectation values of Pauli-Z observables and optimized using classical gradient descent, where the objective function is the mean squared error. The trained quantum model is designed for multi-class classification, specifically distinguishing between meningioma, glioma tumors, pituitary tumors, and the absence of a tumor. The outcome of this proposed work demonstrates the VQC’s advantage over classical baselines, including Random Forest, and Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). The VQC achieved the highest accuracy of 95% and the best robustness (Specificity 0.983). Crucially, the VQC accomplished this using only 24 trainable parameters, showcasing a significant increase in parameter efficiency compared to the classical models. The novelty of this work is the quantitative demonstration that a custom-designed, resource-efficient quantum model can outperform established classical deep learning methods in both performance and efficiency for complex medical image classification. The VQC provides a 3.5-fold gain in parameter efficiency, achieving the highest accuracy of 95% using only 24 trainable parameters. This efficiency is enabled by the novel application of amplitude encoding to the medical images, which compresses 16 features into just 4 qubits. The VQC demonstrates robustness vital for medical diagnostics, achieving a Specificity of 0.983. Its shallow architecture is also NISQ-friendly and remains stable by avoiding the Barren Plateau issue (Gradient Norm: 0.11), validating the path toward quantum advantage in diagnostic tools. The results of this approach highlight the potential amplitude encoding in quantum machine learning, as a feasible and effective method for classifying real-world medical images. This work also opens avenues for future hybrid quantum-classical diagnostic systems that could utilize richer quantum state encodings for significant gains in computational efficiency and accuracy.