<p>In recent years, quantum computing overcomes the limitations of traditional computing for solving complex problems which includes image classification tasks. This article aims to introduce a quantum convolution method that can classify brain tumor at a comparable accuracy in less time, with enhanced secure image transfer. The proposed study uses quantum-based deep learning approach QCONV (Quantum Convolution Model) for brain tumor detection. It also ensures secure image transfer between medical entities using cryptographic techniques. The dataset is categorized into meningioma, glioma, and pituitary tumor. The proposed work is divided to two phases where in phase 1 encryption and decryption of images is performed by Advanced Encryption Standard (AES) in Cipher Feedback (CFB) mode to ensure the security and confidentiality of brain tumor MRI images. During decryption AES cipher is then reinitialized with the reconstructed key and IV, allowing the encrypted data to be processed and converted back into its original binary form. In phase 2 the input images are mapped to quantum space using two rotation gates which rotates the qubits over the Bloch sphere and a controlled gate that enables entanglement which further enhances quantum-based classification using QCONV (Quantum Convolution Network). A performance comparison of QCONV with classical deep learning architecture, including DenseNet121, ResNet50, VGG16, and InceptionV3 to evaluate its effectiveness in brain tumor classification. Quantum Convolution model with 4 qubits and quantum depth of 5 has shown accuracy of 97% with execution time for training as 5.97 s which is optimal when compared with classical models. The classical models execute in <InlineEquation ID="IEq1"> <EquationSource Format="TEX">${O(n}^{2})$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msup> <mrow> <mi>O</mi> <mo stretchy="false">(</mo> <mi>n</mi> </mrow> <mn>2</mn> </msup> <mrow> <mo stretchy="false">)</mo> </mrow> </mrow> </math></EquationSource> </InlineEquation> operations whereas the quantum models exhibit <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$O(\text{l}\text{o}\text{g}(n))$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>O</mi> <mo stretchy="false">(</mo> <mtext>log</mtext> <mo stretchy="false">(</mo> <mi>n</mi> <mo stretchy="false">)</mo> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation><i>.</i> The comparative study refers to the asymptotic scaling of the quantum circuit depth and the number of qubits under an amplitude-style state-encoding assumption, and not to the measured regular execution time. The results demonstrate that the proposed QCONV outperforms the existing classical deep learning models in terms of accuracy, sensitivity, specificity, and F1 score.</p>

Quantum-Based Deep Learning Approach for Brain Tumour MRI Detection

期刊论文
发表日期：

2026年8月5日

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M. Diviya,
M. Subramanian,
P. Divya,
R. Utthirakumari

摘要

In recent years, quantum computing overcomes the limitations of traditional computing for solving complex problems which includes image classification tasks. This article aims to introduce a quantum convolution method that can classify brain tumor at a comparable accuracy in less time, with enhanced secure image transfer. The proposed study uses quantum-based deep learning approach QCONV (Quantum Convolution Model) for brain tumor detection. It also ensures secure image transfer between medical entities using cryptographic techniques. The dataset is categorized into meningioma, glioma, and pituitary tumor. The proposed work is divided to two phases where in phase 1 encryption and decryption of images is performed by Advanced Encryption Standard (AES) in Cipher Feedback (CFB) mode to ensure the security and confidentiality of brain tumor MRI images. During decryption AES cipher is then reinitialized with the reconstructed key and IV, allowing the encrypted data to be processed and converted back into its original binary form. In phase 2 the input images are mapped to quantum space using two rotation gates which rotates the qubits over the Bloch sphere and a controlled gate that enables entanglement which further enhances quantum-based classification using QCONV (Quantum Convolution Network). A performance comparison of QCONV with classical deep learning architecture, including DenseNet121, ResNet50, VGG16, and InceptionV3 to evaluate its effectiveness in brain tumor classification. Quantum Convolution model with 4 qubits and quantum depth of 5 has shown accuracy of 97% with execution time for training as 5.97 s which is optimal when compared with classical models. The classical models execute in ${O(n}^{2})$ ${O (n}^{2})$ operations whereas the quantum models exhibit $O(\text{l}\text{o}\text{g}(n))$ $O (log (n))$ . The comparative study refers to the asymptotic scaling of the quantum circuit depth and the number of qubits under an amplitude-style state-encoding assumption, and not to the measured regular execution time. The results demonstrate that the proposed QCONV outperforms the existing classical deep learning models in terms of accuracy, sensitivity, specificity, and F1 score.