Quantum Convolutional Neural Networks for Medical Image Diagnosis
Asha S Manek, K Shakthi Ganesh, Mohammad Abuzar +3 more
Recently, Quantum Computing has started being widely used for classical problem solving. We can use Quantum mechanics principles for problem solving. CNN (Convolutional Neural Network) had been used in classical computing and problem solving for many years, the implementation of this neural network model in Quantum devices and circuits is known as QCNN (Quantum Convolutional Neural Network). QCNNs are built using quantum circuits. QCNNs can read complicated pictures more quickly, they are allegedly better at extracting features and identifying patterns, these features can make it extremely good at image classification. By analyzing multiple features at the same time, QCNNs process data faster than classical CNNs and reduce the reliance on big datasets, therefore it addresses difficulties related to limited data. We can also see how robust QCNN is to noise, maintaining good accuracy despite many changes in image quality. Good speed and accuracy are demonstrated in experimental results on training datasets, indicating that QCNNs have the potential to transform medical diagnoses and enhance patient outcomes. We have made use of the chest x-ray image dataset which have three labels, Covid, Pneumonia, and Normal.
IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)•Sep 26, 2025•0 citations