Abstract Arecanut X‐ray images accurately represent their internal structure. A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. The investigation explores various transfer learning models with different sizes to identify the most suitable one for achieving enhanced accuracy. The Shufflenet model with a scale factor of 2.0 attains the highest classification accuracy of 97.72% using the QCNN approach, with a model size of 28.40 MB. Out of the 12 transfer learning models tested, 9 exhibit improved classification accuracy when using QCNN models compared to the traditional CNN‐based transfer learning approach. Consequently, the exploration of CNN and QCNN‐based classification reveals that QCNN outperforms traditional CNN models in accuracy within the transfer learning framework. Further experiments with qubits suggest that utilising 4 qubits is optimal for classification operations in this context.
Abstract Arecanut X‐ray images accurately represent their internal structure. A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. The investigation explores various transfer learning models with different sizes to identify the most suitable one for achieving enhanced accuracy. The Shufflenet model with a scale factor of 2.0 attains the highest classification accuracy of 97.72% using the QCNN approach, with a model size of 28.40 MB. Out of the 12 transfer learning models tested, 9 exhibit improved classification accuracy when using QCNN models compared to the traditional CNN‐based transfer learning approach. Consequently, the exploration of CNN and QCNN‐based classification reveals that QCNN outperforms traditional CNN models in accuracy within the transfer learning framework. Further experiments with qubits suggest that utilising 4 qubits is optimal for classification operations in this context.
Accurate and efficient classification of bone fractures from X-ray images is crucial for timely diagnosis, effective treatment planning, and improved patient outcomes in orthopedic medicine. Convolutional neural networks (CNNs) have demonstrated their ability to automatically extract relevant features from X-ray images, making them a powerful tool for the classification of complex bone fracture patterns. However, as the complexity of the classification task increases, the computational requirements of classical CNNs also grow rapidly. This poses a challenge in high-stakes medical applications, where the accuracy and efficiency of the classification system are paramount. In this work, we present a quantum convolutional neural network (QCNN) for the classification of bone fractures from X-ray images. A quantum convolution layer is integrated as the first layer of the network, which leverages the unique properties of quantum mechanics to extract more informative features from the input X-ray images. A traditional CNN network follows this quantum-based feature extraction step to perform the final bone fracture classification. We tested the proposed QCNN model on the Bone Fracture Multi-Region X-ray Data, a benchmark dataset for evaluating bone fracture classification systems. The QCNN architecture achieved an impressive 96% accuracy and 95.81% F1-score, demonstrating its superior performance compared to state-of-the-art classical CNN models.
A common model for classifying images is the convolutional neural network (CNN), which has the benefit of effectively using data correlation information. Despite their remarkable success, classical CNNs may face challenges in achieving further improvements in accuracy, computational efficiency, explainability, and generalization. However, if the specified data dimension or model grows too large, CNN becomes difficult to train effectively with a slowdown processing. In order to address a problem using CNN utilizing quantum computing, Quantum Convolutional Neural Network (QCNN) proposes a novel quantum solution or enhances the functionality of an existing learning model in terms of processing time during training. This paper presents a comparative analysis between classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture tailored for image-based tasks, emphasizing the adaptability and versatility of quantum circuits in enhancing feature extraction capabilities and then final accuracy and processing time. A MNIST and covidx-cxr3 datasets was used to train quantum-CNN models, and the results of these comparisons were made with traditional CNN performance. The results demonstrate that the suggested QCNN beat the traditional CNN in terms of recognition accuracy and processing speed (process time) when combined with cutting-edge feature extraction techniques. This superiority is particularly evident when trained on the covidx-cxr3 dataset, highlighting the potential for quantum computing to revolutionize image classification tasks.
Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter several challenges, including limitations in accuracy improvement, computational efficiency, explain ability, and generalization. As data dimensionality or model complexity increases, CNNs often become more difficult to train efficiently, leading to slower processing times. To overcome these challenges, Quantum Convolutional Neural Networks (QCNNs) offer a promising quantum-based approach to enhance existing models, particularly by improving training efficiency and reducing processing times. This paper provides a comparative analysis of classical CNNs and a novel quantum circuit architecture tailored for image-based tasks. The analysis emphasizes the adaptability and potential of quantum circuits to enhance feature extraction, recognition accuracy, and processing efficiency.We applied various QCNN architectures to the Chinese-MNIST dataset and compared their performance with traditional CNN models. Although QCNNs achieved slightly lower accuracy, they demonstrated significantly greater stability. These findings high- light QCNNs’ potential to overcome the computational limitations of conventional deep learning, offering a promising pathway for quantum-enhanced solutions in computer vision. Future work will focus on refining quantum circuit designs, developing hybrid quantum-classical models, and extending QCNN applications to larger and more diverse datasets.
In this work, we investigate the performance of hybrid quantum-classical convolutional neural networks (QCNNs) in medical image classification under the presence of adversarial attacks. Adversarial attacks pose a significant threat to image classification models, since they introduce subtle perturbations to input images, leading to incorrect predictions. Specifically, in this work, 10 QCNNs with their corresponding classic Convolutional Neural Networks (CNNs) counterparts are comparatively examined for their robustness, on two different medical image datasets using transfer learning, under two different adversarial attacks. In a subsequent step, the implementation of training models from scratch is also evaluated. With transfer learning on the X-ray dataset, CNN and QCNN models achieve high and comparable performance across most backbones. On the histopathological dataset, CNNs and QCNNs often generalize worse compared to the X-ray dataset, indicating head overfitting under domain shift. The adversarial evaluation results highlight that most models' robustness comes from the backbone rather than the head. QCNN models perform comparably to CNNs under attack and show improvements when trained from scratch. Overall, the results indicate that integrating quantum circuit mechanisms into classical CNN architectures can enhance the robustness of models in medical imaging tasks, representing a promising domain that warrants further investigation.
In recent years, there has been a growing interest in leveraging the unique properties of quantum computing to develop novel machine learning algorithms and architectures. This research paper presents an investigation of quantum convolutional neural networks (QCNNs), which leverage the unique properties of quantum computing to potentially improve the accuracy and efficiency of image classification tasks. Specifically, the paper explores three different QCNN architectures, including a pure quantum-based QCNN, a hybrid QCNN with a single quantum convolution layer, and a hybrid convolutional architecture with multiple quantum filters. We tested the models on MNIST dataset and the results of the study demonstrate that hybrid architectures that combine quantum and classical processing are more effective than pure quantum-based architectures in image classification tasks. In particular, the third model, the Hybrid Convolution with Multiple Quantum Filters, achieved the highest test set accuracy of 92.7%. The use of multiple quantum filters in conjunction with a classical neural network resulted in enhanced accuracy and efficiency in image classification tasks, highlighting the potential of hybrid architectures for future applications in machine learning tasks.
This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome this challenge by utilizing qubits to represent data in a quantum environment and applying CNN structures to quantum computers. The time and accuracy of QCNNs are compared with classical CNNs and ANN models under different conditions such as batch size and input size. The maximum complexity level that QCNNs can handle in terms of these parameters is also investigated. The analysis shows that QCNNs have the potential to outperform both classical CNNs and ANN models in terms of accuracy and efficiency for certain applications, demonstrating their promise as a powerful tool in the field of machine learning.