Quantum-Enhanced Neural Networks For Brain Stroke Classification: A Comparative Study Of Classical And Hybrid Approaches
Keywords:
Quantum machine learning, Hybrid quantum-classical networks, Medical image classifi- cation, Brain stroke detection, · Deep learning.Abstract
This study presents a comparative analysis of traditional deep learning and a novel quantum- assisted neural network for brain stroke identification on brain CT images. We compared traditional convolutional neural network (CNN) architectures such as ResNet18, VGG16, MobileNetV2 and In- ceptionV3. We found a hybrid quantum-classical approach which incorporated ResNet and MobileNet feature extraction tools and combined the extracted features in a quantum circuit for processing. The accuracy obtained for the quantum-assisted model on our validation dataset was 99.15%, which is a significant improvement from the traditional models: ResNet18 98.30%, VGG16 98.30%, MobileNetV2 97.02%, and InceptionV3 97.87%. These results indicate that there are likely advantages in enhanced medical image analysis by incorporating quantum techniques that may be useful in high stakes diagnos- tic like stroke detection. The hybrid architecture reflected some unique dynamic model assignment of importance for prediction assignment, with ResNet features contributing about 51.17% and MobileNet features contributing 48.83% to the overall predictions.
