Martis Jason Elroy, M S Sannidhan, R Balasubramani, Mutawa A M, Murugappan M
Department of ISE, NMAM Institute of Technology, Nitte Deemed to be University, Udupi 574110, Karnataka, India.
Department of CSE, NMAM Institute of Technology, Nitte Deemed to be University, Udupi 574110, Karnataka, India.
Bioengineering (Basel). 2024 Aug 7;11(8):799. doi: 10.3390/bioengineering11080799.
Lung cancer, the second most common type of cancer worldwide, presents significant health challenges. Detecting this disease early is essential for improving patient outcomes and simplifying treatment. In this study, we propose a hybrid framework that combines deep learning (DL) with quantum computing to enhance the accuracy of lung cancer detection using chest radiographs (CXR) and computerized tomography (CT) images. Our system utilizes pre-trained models for feature extraction and quantum circuits for classification, achieving state-of-the-art performance in various metrics. Not only does our system achieve an overall accuracy of 92.12%, it also excels in other crucial performance measures, such as sensitivity (94%), specificity (90%), F1-score (93%), and precision (92%). These results demonstrate that our hybrid approach can more accurately identify lung cancer signatures compared to traditional methods. Moreover, the incorporation of quantum computing enhances processing speed and scalability, making our system a promising tool for early lung cancer screening and diagnosis. By leveraging the strengths of quantum computing, our approach surpasses traditional methods in terms of speed, accuracy, and efficiency. This study highlights the potential of hybrid computational technologies to transform early cancer detection, paving the way for wider clinical applications and improved patient care outcomes.
肺癌是全球第二常见的癌症类型，带来了重大的健康挑战。早期检测这种疾病对于改善患者预后和简化治疗至关重要。在本研究中，我们提出了一种将深度学习（DL）与量子计算相结合的混合框架，以提高使用胸部X光片（CXR）和计算机断层扫描（CT）图像进行肺癌检测的准确性。我们的系统利用预训练模型进行特征提取，并使用量子电路进行分类，在各项指标上均达到了领先水平。我们的系统不仅总体准确率达到了92.12%，在其他关键性能指标上也表现出色，如灵敏度（94%）、特异性（90%）、F1分数（93%）和精确率（92%）。这些结果表明，与传统方法相比，我们的混合方法能够更准确地识别肺癌特征。此外，量子计算的融入提高了处理速度和可扩展性，使我们的系统成为早期肺癌筛查和诊断的有前途的工具。通过利用量子计算的优势，我们的方法在速度、准确性和效率方面超越了传统方法。这项研究突出了混合计算技术在变革早期癌症检测方面的潜力，为更广泛的临床应用和改善患者护理结果铺平了道路。
