Exploring the Power of Quantum Convolutional Neural Networks for Brain MRI Image Classification

Various neurological problems such as brain tumors, strokes, and Alzheimer's disease can all be detected and treated with the help of medical imaging. The level of image analysis determines how accurate and reliable a diagnosis made using Magnetic Resonance Imaging (MRI) will be. As a result, the intricate design of brain structures as well as the presence of noise and aberrations in the recorded pictures might make it difficult at times. This study proposes a Quantum Convolutional Neural Networks (QCNN)-based classification model for brain MRI images. Preprocessing and QCNN-based categorization comprise the two key components of the proposed model. A publicly available real brain MRI dataset is used to test the proposed model. The proposed model is extremely promising, according to the experimental findings. The results also demonstrated that the proposed QCNN outperforms its traditional convolutional neural network counterpart. The proposed model looks very promising, according to the experimental findings. The results additionally demonstrated that the proposed QCNN outperforms its traditional convolutional neural network counterpart. It achieved a 96.1% total accuracy.