Roy, S.K.Rudra, B.2026-02-042024International Journal of Imaging Systems and Technology, 2024, 34, 1, pp. -8999457https://doi.org/10.1002/ima.23015https://idr.nitk.ac.in/handle/123456789/21469Finding brain tumors is a crucial step in medical diagnosis that can have a big impact on how patients turn out. Conventional detection techniques can be laborious and demand a lot of computing power. Brain tumor detection could be made more effective and precise, thanks to the quickly developing field of quantum computing. In this article, we propose a quantum machine learning (QML)-based method for brain tumor extraction and detection based on quantum computing. To implement our strategy, we use a Hybrid Quantum-Classical Convolutional Neural Network (HQC-CNN) that has been trained using a collection of brain MRI images. Additionally, we employ Batchwise Q-Means++ Clustering for segmenting the images and a Max-cut approach with Adiabatic Quantum Computation (AQC) to extract the tumor region from the segmented MRI image. Our results highlight the strength of Quanvolutional Layer in Neural Network and reduced time complexity exponentially or quadratically in clustering and max-cut algorithms respectively and see the potential of quantum computing for improving the accuracy and speed of medical diagnosis and have implications for the future of healthcare technology. © 2024 Wiley Periodicals LLC.BrainComputing powerConvolutional neural networksImage classificationImage segmentationLearning algorithmsMachine learningMagnetic resonance imagingMultilayer neural networksNetwork layersImage segmentaiaMachine-learningMAX CUTMax-cutMax-cut algorithmMeans clusteringQ-mean clusteringQuantum annealingQuantum ComputingQuantum machine learningQuantum machinesTumor analyseTumors