This study explores the integration of deep learning (DL) and quantum-inspired machine learning techniques for the binary classification of brain tumors using MRI images, focusing on distinguishing tumor from non-tumor cases. A novel hybrid architecture combining Convolutional Neural Networks (CNNs) and Quantum Neural Networks (QNNs) is proposed to enhance diagnostic accuracy and computational efficiency. The performance of the hybrid CNN-QNN model is compared to traditional DL approaches, including classic CNNs and transfer learning models, based on accuracy, sensitivity, specificity, and computational cost. Results demonstrate that the CNN-QNN model consistently outperforms conventional methods, achieving higher diagnostic precision while reducing computational overhead. This research provides valuable insights into the transformative potential of quantum machine learning in medical imaging, offering a robust and efficient solution for improving early tumor detection in clinical settings. By accelerating diagnostic processes, the study highlights a promising future for quantum-inspired technologies in healthcare. Quantum Neural Networks (QNNs) leverage the principles of quantum mechanics to enhance neural network performance, especially in domains requiring high computational power. This project explores the application of QNNs for brain tumor detection, combining classical neural networks (CNNs) with quantum-inspired techniques. The motivation for this work lies in optimizing diagnostic accuracy through hybrid quantum-classical models.