A novel qVGG-4 model for optimizing a parameterized quantum circuit in a quantum-IoT-based brain tumor detection and monitoring system.

BACKGROUND: Since brain tumors (BTs) require early detection for timely and effective treatment planning, this study presents two quantum deep learning (Q-DL) approaches: a quantum Convolutional Neural Network (CNN) and a quantum Vision Transformer (ViT). The implications of Q-DL for disease detection in medical images are limited, and previous studies have suggested that Q-DL has unsatisfactory accuracy. METHODS: To fill this gap, four models, (1) quantum CNN (Q-CNN), (2) hybrid quantum CNN (HQ-CNN), (3) Q-ViT, and (4) hybrid quantum ViT (HQ-ViT), were developed and tested on four BT-MRI datasets. BT patients demand real-time monitoring as they suffer from headaches, seizures, cognitive and behavioral changes, and neurological deficits. Therefore, we propose a smart brain tumor management system (SBTM) for real-time monitoring. RESULTS: Trained on the three brain tumor datasets using the Adam optimizer and five-fold cross-validation, the hybrid Q-DL, HQ-CNN, achieved an accuracy of 97%, and HQ-ViT achieved 96% in (tumor, no tumor) classification, which outperforms the parameterized quantum circuit (PQC)-based Q-DLs. The high accuracy of hybrid models continues: in 3 classes, 44% by CNN and 28% by HQ-ViT, and in 4 classes, 49% by HQ-CNN and 23% by HQ-ViT. The increased accuracy of hybrid models continues in the detection and classification test dataset of brain tumor MRI images. The results suggest that combining qVGG-4 with PQC in both CNNs and ViTs yields more powerful feature extraction than either alone. CONCLUSIONS: The main novelty of this study is the use of a qVGG-4 model that optimizes PQC. Whereas traditional CNNs struggle with small tumors, the HQ-CNN and HQ-ViT methods achieve impressive accuracy even on 28 × 28-pixel images. This result solves the issue of handling complex lesion detection in small areas and accelerates the model training time. The high accuracy in detecting and classifying unseen MRI images is a significant contribution to SBTM. In clinical settings, a machine learning model is expected to perform well in detecting and classifying new MRI images.