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<p>The problem of detecting tuberculosis (TB) from chest X-ray (CXR) images was addressed by developing a convolutional neural network (CNN) model. The CNN was trained using two publicly available chest radiograph datasets from Himachal Pradesh, India. To evaluate its performance, the CNN model was compared with a transfer learning-based technique that utilized several pre-trained CNNs. The results showed that the CNN model outperformed the transfer learning approach. The study further explored the integration of quantum algorithms in image classification. It introduced a novel hybrid quantum–classical computing paradigm, wherein classically challenging elements of an algorithm are handled by a quantum computer. Specifically, a hybrid quantum–classical image classification technique, inspired by CNNs and called the quanvolutional neural network (QNN), was used for diagnosing TB from chest X-rays. This new QNN variant employed novel enhanced quantum representation (NEQR) image encoding to transform pixel values into quantum states. The QNN model was trained on the same two chest radiograph datasets as the CNN model. The QNN demonstrated superior performance to the CNN model, with a validation accuracy of 87% during training. These findings suggest that hybrid quantum–classical image classification algorithms can surpass traditional methods in diagnosing tuberculosis from chest X-rays. From the results, the Exception, ResNet50, and VGG16 models performed exceptionally well in Quantum CNN models with image augmentation, achieving over 89% accuracy, precision, sensitivity, and F1-score for TB detection. The study suggests that a larger dataset could improve model precision and reliability. Differences in outcomes compared to previous studies may stem from using simulators of flawless quantum computers in earlier research. This study highlights the potential of integrating a quantum circuit with classical CNNs for TB diagnosis from chest X-rays using NEQR image encoding, providing a foundation for future research.</p>

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Meta-analysis of quantum convolutional neural networks for automated tuberculosis screening on chest x-rays

  • 期刊论文

  • 发表日期：

    2025年3月26日

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  • Anurag Rana,
  • Dimple Kumar Bhaglani,
  • Pankaj Vaidya,
  • Yu-Chen Hu

摘要

The problem of detecting tuberculosis (TB) from chest X-ray (CXR) images was addressed by developing a convolutional neural network (CNN) model. The CNN was trained using two publicly available chest radiograph datasets from Himachal Pradesh, India. To evaluate its performance, the CNN model was compared with a transfer learning-based technique that utilized several pre-trained CNNs. The results showed that the CNN model outperformed the transfer learning approach. The study further explored the integration of quantum algorithms in image classification. It introduced a novel hybrid quantum–classical computing paradigm, wherein classically challenging elements of an algorithm are handled by a quantum computer. Specifically, a hybrid quantum–classical image classification technique, inspired by CNNs and called the quanvolutional neural network (QNN), was used for diagnosing TB from chest X-rays. This new QNN variant employed novel enhanced quantum representation (NEQR) image encoding to transform pixel values into quantum states. The QNN model was trained on the same two chest radiograph datasets as the CNN model. The QNN demonstrated superior performance to the CNN model, with a validation accuracy of 87% during training. These findings suggest that hybrid quantum–classical image classification algorithms can surpass traditional methods in diagnosing tuberculosis from chest X-rays. From the results, the Exception, ResNet50, and VGG16 models performed exceptionally well in Quantum CNN models with image augmentation, achieving over 89% accuracy, precision, sensitivity, and F1-score for TB detection. The study suggests that a larger dataset could improve model precision and reliability. Differences in outcomes compared to previous studies may stem from using simulators of flawless quantum computers in earlier research. This study highlights the potential of integrating a quantum circuit with classical CNNs for TB diagnosis from chest X-rays using NEQR image encoding, providing a foundation for future research.