Detection of Brain Tumor Using Enhanced Deep Fuzzy Logic and Accelerated Quantum CNN

Brain cancer examination is a critical task in medical photo shooting that necessitates precise and efficient diagnostic methods. This study introduces an upgraded framework that combines deep type fuzzy based logic with the quantum convolutional type neural network (QCNN) that is a fast quantum-based network to enhance tumor detection and classification. The deep fuzzy logic unit further enhances image preprocessing by means of accurately handling uncertainty, in turn, contrast enhancement and segmentation of tumor regions with high accuracy. This processed data is then input to the fast QCNN, which employs quantum parallelism to accelerate feature extraction and classification. The proposed system summed up fuzzy logic’s adaptability with quantum computing’s computational effectiveness, thereby, optimizing learning performance while enhancing decision-making capacity. The integration of two methodologies confers not only improved stability of the model and decreased computational complexity but also faster inference time therefore it is a suitable candidate for real-time medical applications.