High-Performance Computing-Based Brain Tumor Detection Using Parallel Quantum Dilated Convolutional Neural Network
Abstract
: We present a high-performance quantum computing-based model for brain tumor detection based on a Parallel Quantum Dilated Convolutional Neural Network (PQDCNN) framework. It uses Fuzzy Local Information C-Means (FLICM) clustering for better segmentation, to execute better than the normal K-means. The data preprocessing processes are median filtering and data augmentation. Deep fusion of FREAK descriptors, GLCM texture features, and deep CNN representations Our PQDCNN model achieves high classification performance on both datasets, outperforming state-of-the-art CNNs on BraTS2020 and Figshare datasets, showing the potential of quantum-inspired deep learning.
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