Application of quantum CNN empowered a brain app to detect a brain tumor

Abstract

High rates of sickness and death from brain tumors make them a major public health problem. This shows how important early discovery and correct classification are for effective treatment. Traditional ways of diagnosing, like having doctors read Magnetic Resonance Imaging (X-RAY) scans, take a lot of time, are prone to mistakes, and depend on expert knowledge. Convolutional Neural Networks (CNNs) have come a long way and show a lot of potential for automating the detection and classification of brain tumors, with great accuracy and speed. Classical CNNs, on the other hand, have a lot of problems, such as high computational costs, the ability to overfit, and problems with processing big datasets quickly. Quantum Convolutional Neural Networks (QCNNs) are a possible answer because they use quantum computing ideas like superposition and entanglement to improve the ability to extract features and lower the amount of work that needs to be done on the computer. This study used a large sample of 4,599 X-RAY images to compare how well QCNNs and classical CNNs work at finding brain tumors. The QCNN architecture includes quantum convolutional layers and pooling layers that are meant to use quantum effects to make picture classification more accurate. The classical CNN design, on the other hand, is made up of standard convolutional layers, pooling layers, and fully connected layers that are optimized using standard deep learning techniques. The X-RAY dataset was used to train and test both models extensively, and accuracy, precision, recall, and the F1-score were used as performance measures that were calculated and analyzed. QCNN did much better than the standard CNN, scoring 97.82% on the test, which was a big improvement. Using confusion matrices and Receiver Operating Characteristic curves, we were able to get a lot of information about each model's diagnostic skills, strengths, and weaknesses.