Spine Image Reconstruction and Lesion Classification Based on Transfer Learning and Quantum Convolutional Neural Network

Spine CT image reconstruction and lesion classification are crucial in diagnosing spine disorders, supporting treatment through automated lesion detection. Leveraging advancements in machine learning, this paper introduces a novel approach using transfer learning and a Quantum Convolutional Neural Network (QCNN) to improve accuracy in spine image analysis. First, the study applies MobileNetv2 and ResNet50-based transfer learning models for spine image reconstruction, comparing their results to select the best-performing model. For lesion classification, a QCNN model is proposed, with its outputs passed into a traditional neural network comprising convolutional, max-pooling, and residual layers. The proposed methods outperform prior models and other pre-trained machine learning classifiers, demonstrating enhanced accuracy and efficiency in spine lesion classification. This approach offers valuable support for radiologists, suggesting an efficient automated solution for identifying and classifying spinal lesions in CT images.