Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images

Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography Images Jason Elroy Martis Department of ISE, NMAM Institute of Technology, Nitte Deemed to be University, Udupi 574110, Karnataka, India http://orcid.org/0000-0001-8434-3577 Sannidhan M S Department of CSE, NMAM Institute of Technology, Nitte Deemed to be University, Udupi 574110, Karnataka, India http://orcid.org/0000-0003-2871-3451 Balasubramani R Department of ISE, NMAM Institute of Technology, Nitte Deemed to be University, Udupi 574110, Karnataka, India http://orcid.org/0000-0001-6394-6774 A. M. Mutawa Computer Engineering Department, College of Engineering and Petroleum, Kuwait University, Safat 13060, Kuwait Computer Sciences Department, University of Hamburg, 22527 Hamburg, Germany http://orcid.org/0000-0002-5707-2692 M. Murugappan Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai 600117, Tamil Nadu, India Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Arau 02600, Malaysia http://orcid.org/0000-0002-5839-4589

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.
08 07 2024 799 bioengineering11080799 https://creativecommons.org/licenses/by/4.0/ 10.3390/bioengineering11080799 https://www.mdpi.com/2306-5354/11/8/799 https://www.mdpi.com/2306-5354/11/8/799/pdf Althubiti Ensemble learning framework with GLCM texture extraction for early detection of lung cancer on CT images Comput. Math. Methods Med. 2022 10.1155/2022/2733965 2022 2733965 Westeel Chest CT scan plus x-ray versus chest x-ray for the follow-up of completely resected non-small-cell lung cancer (IFCT-0302): A multicentre, open-label, randomised, phase 3 trial Lancet Oncol. 2022 10.1016/S1470-2045(22)00451-X 23 1180 Saber A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique IEEE Access 2021 10.1109/ACCESS.2021.3079204 9 71194 Sadad Brain tumor detection and multi-classification using advanced deep learning techniques Microsc. Res. Tech. 2021 10.1002/jemt.23688 84 1296 Hu Deep learning for image-based cancer detection and diagnosis A survey Pattern Recognit. 2018 10.1016/j.patcog.2018.05.014 83 134 10.1038/s41598-021-84630-x Chaunzwa, T.L., Hosny, A., Xu, Y., Shafer, A., Diao, N., Lanuti, M., Christiani, D.C., Mak, R.H., and Aerts, H.J.W.L. (2021). Deep learning classification of lung cancer histology using CT images. Sci. Rep., 11. Lakshmanaprabu Optimal deep learning model for classification of lung cancer on CT images Futur. Gener. Comput. Syst. 2019 10.1016/j.future.2018.10.009 92 374 Wei A quantum convolutional neural network on NISQ devices AAPPS Bull. 2022 10.1007/s43673-021-00030-3 32 2 Zhao Qdnn: Deep neural networks with quantum layers Quantum Mach. Intell. 2021 10.1007/s42484-021-00046-w 3 15 Beer Training deep quantum neural networks Nat. Commun. 2020 10.1038/s41467-020-14454-2 11 808 10.1109/I-SMAC52330.2021.9640678 Kora, P., Mohammed, S., Surya Teja, M.J., Usha Kumari, C., Swaraja, K., and Meenakshi, K. (2021, January 11–13). Brain Tumor Detection with Transfer Learning. Proceedings of the 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India. Mohite Application of transfer learning technique for detection and classification of lung cancer using CT images Int. J. Sci. Res. Manag. 2021 9 621 Sundar Transfer learning approach in deep neural networks for uterine fibroid detection Int. J. Comput. Sci. Eng. 2022 25 52 10.1109/ICECCPCE46549.2019.203771 Alkassar, S., Abdullah, M.A.M., and Jebur, B.A. (2019, January 13–14). Automatic brain tumour segmentation using fully convolution network and transfer learning. Proceedings of the 2019 2nd International Conference on Electrical, Communication, Computer, Power and Control Engineering (ICECCPCE), Mosul, Iraq. 10.3390/healthcare10061058 Humayun, M., Sujatha, R., Almuayqil, S.N., and Jhanjhi, N.Z. (2022). A transfer learning approach with a convolutional neural network for the classification of lung carcinoma. Healthcare, 10. Wang Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy Open Med. 2020 10.1515/med-2020-0028 15 190 10.1371/journal.pone.0200721 Nishio, M., Sugiyama, O., Yakami, M., Ueno, S., Kubo, T., Kuroda, T., and Togashi, K. (2018). Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. PLoS ONE, 13. 10.1109/CBMS.2018.00050 Da Nóbrega, R.V.M., Peixoto, S.A., da Silva, S.P.P., and Rebouças Filho, P.P. (2018, January 18–21). Lung nodule classification via deep transfer learning in CT lung images. Proceedings of the 2018 IEEE 31st international symposium on computer-based medical systems (CBMS), Karlstad, Sweden. 10.1145/3478905.3478995 Phankokkruad, M. (2021, January 23–25). Ensemble transfer learning for lung cancer detection. Proceedings of the 2021 4th International Conference on Data Science and Information Technology, Shanghai, China. Saikia An automatic lung nodule classification system based on hybrid transfer learning approach SN Comput. Sci. 2022 10.1007/s42979-022-01167-0 3 272 Bhandary Deep-learning framework to detect lung abnormality—A study with chest X-Ray and lung CT scan images Pattern Recognit. Lett. 2019 10.1016/j.patrec.2019.11.013 129 271 10.1016/j.compbiomed.2021.104348 Ibrahim, D.M., Elshennawy, N.M., and Sarhan, A.M. (2021). Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Comput. Biol. Med., 132. 10.1038/s41598-021-99015-3 Yang, D., Martinez, C., Visuña, L., Khandhar, H., Bhatt, C., and Carretero, J. (2021). Detection and analysis of COVID-19 in medical images using deep learning techniques. Sci. Rep., 11. Kamil A deep learning framework to detect COVID-19 disease via chest X-ray and CT scan images Int. J. Electr. Comput. Eng. 2021 11 844 10.1016/j.cmpbup.2022.100054 Shyni, H.M., and Chitra, E. (2022). A comparative study of X-ray and CT images in COVID-19 detection using image processing and deep learning techniques. Comput. Methods Programs Biomed. Updat., 2. Chen Quantum convolutional neural network for image classification Pattern Anal. Appl. 2023 10.1007/s10044-022-01113-z 26 655 Sebastianelli On circuit-based hybrid quantum neural networks for remote sensing imagery classification IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 2021 10.1109/JSTARS.2021.3134785 15 565 Wang Development of variational quantum deep neural networks for image recognition Neurocomputing 2022 10.1016/j.neucom.2022.06.010 501 566 Mogalapalli Classical–quantum transfer learning for image classification SN Comput. Sci. 2022 10.1007/s42979-021-00888-y 3 20 Subbiah Quantum transfer learning for image classification TELKOMNIKA (Telecommun. Comput. Electron. Control) 2023 10.12928/telkomnika.v21i1.24103 21 113 Henderson Quanvolutional neural networks: Powering image recognition with quantum circuits Quantum Mach. Intell. 2020 10.1007/s42484-020-00012-y 2 2 Kayan, C.E., Koksal, T.E., Sevinc, A., and Gumus, A. (2023). Deep reproductive feature generation framework for the diagnosis of COVID-19 and viral pneumonia using chest X-ray images. arXiv. Sannidhan Performance enhancement of generative adversarial network for photograph–sketch identification Soft Comput. 2023 10.1007/s00500-021-05700-w 27 435 10.1109/CVPR46437.2021.01352 Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 20–25). Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA. 10.3389/fgene.2022.980338 Ghose, P., Alavi, M., Tabassum, M., Uddin, A., Biswas, M., Mahbub, K., Gaur, L., Mallik, S., and Zhao, Z. (2022). Detecting COVID-19 infection status from chest X-ray and CT scan via single transfer learning-driven approach. Front. Genet., 13. Kallel CT scan contrast enhancement using singular value decomposition and adaptive gamma correction Signal Image Video Process. 2018 10.1007/s11760-017-1232-2 12 905 Sannidhan Detection of Antibiotic Constituent in Aspergillus flavus Using Quantum Convolutional Neural Network Int. J. E-Health Med. Commun. 2023 10.4018/IJEHMC.321150 14 1 Abbas The power of quantum neural networks Nat. Comput. Sci. 2021 10.1038/s43588-021-00084-1 1 403 Hou A partial least squares regression model based on variational quantum algorithm Laser Phys. Lett. 2022 10.1088/1612-202X/ac81b6 19 095204 Chalumuri A hybrid classical-quantum approach for multi-class classification Quantum Inf. Process. 2021 10.1007/s11128-021-03029-9 20 119 Coffey Comment on “Universal quantum circuit for two-qubit transformations with three controlled-NOT gates” and “Recognizing small-circuit structure in two-qubit operators” Phys. Rev. A 2008 10.1103/PhysRevA.77.066301 77 066301 Moore Parallel quantum computation and quantum codes SIAM J. Comput. 2001 10.1137/S0097539799355053 31 799 10.26421/QIC3.2-5 Song, G., and Klappenecker, A. (2002). Optimal realizations of controlled unitary gates. arXiv. Nakaji, K., Tezuka, H., and Yamamoto, N. (2021). Quantum-enhanced neural networks in the neural tangent kernel framework. arXiv. 10.1109/ICTC49870.2020.9289439 Oh, S., Choi, J., and Kim, J. (2020, January 21–23). A tutorial on quantum convolutional neural networks (QCNN). Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Republic of Korea. 10.1109/RTEICT52294.2021.9574030 Rajesh, V., Naik, U.P. (2021, January 27–28). Quantum Convolutional Neural Networks (QCNN) using deep learning for computer vision applications. Proceedings of the 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India. 10.1007/978-3-030-32251-9_42 Zhou, Z., Sodha, V., Rahman Siddiquee, M.M., Feng, R., Tajbakhsh, N., Gotway, M.B., and Liang, J. (2019, January 13–17). Models genesis: Generic autodidactic models for 3d medical image analysis. Proceedings of the Medical Image Computing and Computer Assisted Intervention—MICCAI 2019: 22nd International Conference, Shenzhen, China. Proceedings, Part IV 22, 2019. 10.1016/j.compbiomed.2020.104115 Morid, M.A., Borjali, A., and Del Fiol, G. (2021). A scoping review of transfer learning research on medical image analysis using ImageNet. Comput. Biol. Med., 128. 10.3390/app10134523 Alzubaidi, L., Fadhel, M.A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y., and Oleiwi, S.R. (2020). Towards a better understanding of transfer learning for medical imaging: A case study. Appl. Sci., 10. Veasey Lung nodule malignancy prediction from longitudinal CT scans with Siamese convolutional attention networks IEEE Open J. Eng. Med. Biol. 2020 10.1109/OJEMB.2020.3023614 1 257