A Novel Framework For Brain Disease Classification Using Quantum Convolutional Neural Network

More than 3,410 children and teens (under the age of 20) in India suffer from catastrophic brain disorders and the creation of abnormal brain cells each year, according to recent reports on the country's rapid increase in brain ailments. It is difficult to quantify the survival rate of human patients due to the rarity, variability, and abnormal development of tumors. These patients cannot be accurately predicted using machine learning approaches for early diagnosis. These methods need a huge amount of discriminatory information and processing time based on annotated limited brain image datasets to classify diseases. To solve this problem, we proposed a novel model based on Quantum Convolutional Neural Network techniques for early diagnosis of tumors in a less invasive manner that may reduce the number of operations and more precisely define the kind of therapy needed. The proposed framework uses image processing techniques to remove noise and other specific artifacts from the brain images. After that, the processed images are segmented into distinct regions of interest using machine learning techniques. The suggested framework uses deep quantum approaches to identify brain disorders based on extracted discriminating characteristics from the brain images, such as textures and qubit features, enabling reliable prediction of brain disorders. The proposed system provided 93.99% accuracy for the accurate prediction of brain diseases using classical models and 88.7% accuracy using the Quantum Convolutional Neural Network (QCNN) model.