Quantum-TransNet: Hybrid MRI Brain Tumor Classification

Brain tumors are among the most perilous neurological diseases, and proper and prompt diagnoses are of significant importance in deciding the course of treatment for improving the survival rate of the patients. MRI scan is the gold standard imaging modality that is used to diagnose brain tumors. Most deep learning-based approaches for the classification of brain tumor need an abundance of data along with considerable computational power to train the neural network properly. All these elements make the conventional systems inefficient. In this project, we present the Quantum-TransNet, which is a classical-quantum hybrid model for brain tumor classification using Transfer Learning variational quantum circuit (VQC). The model will classify glioma, Meningioma, Pituitary tumor, and Normal brain using computer-assisted diagnosis to help practitioners with their diagnosis. It is expected that the model will deliver superior performance in classifying brain tumors.