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MLDC: multi-lung disease classification using quantum classifier and artificial neural networks

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Abstract

Lung diseases are one of the most common diseases around the world. The risk of these diseases are more in under-developed and developing countries, where millions of people are battling with poverty and living in polluted air. Chest X-Ray images are helpful screening tool for lung disease detection. However, disease diagnosis requires expert medical professionals. Furthermore, in developing and under-developed nations, the doctor-to-patient ratio is comparatively poor. Deep learning algorithms have recently demonstrated promise in the analysis of medical images and the discovery of patterns. In this current work, we have proposed a model MLDC (Multi-Lung Disease Classification) to detect common lung diseases. It introduces a MLDC feature extraction model with two different new classifiers, considering ANN (an artificial neural network) and QC (a quantum classifier). In this proposed model, tests are performed on the LDD (Lung Disease Dataset), which includes COVID-19, pneumonia, tuberculosis, and a healthy person’s lung from chest X-ray images. Our proposed model achieves an accuracy of 95.6% for MLDC-ANN and 97.5% for MLDC-QC at a lower computational cost.

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References

  1. Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP et al (2018) Deep learning for chest radiograph diagnosis: a retrospective comparison of the chexnext algorithm to practicing radiologists. PLoS Med 15(11):1002686

    Article  Google Scholar 

  2. Liu C, Cao Y, Alcantara M (2017) TX-CNN: detecting tuberculosis in chest X-ray images using convolutional neural network, 2314–2318. Cited By :1

  3. Kesim E, Dokur Z, Olmez T (2019) X-ray chest image classification by a small-sized convolutional neural network. In: 2019 scientific meeting on electrical-electronics biomedical engineering and computer science (EBBT), pp 1–5. https://doi.org/10.1109/EBBT.2019.8742050

  4. Chouhan V, Singh SK, Khamparia A, Gupta D, Tiwari P, Moreira C, Damaševičius R, de Albuquerque VHC (2020) A novel transfer learning based approach for pneumonia detection in chest x-ray images. Appl Sci. https://doi.org/10.3390/app10020559

    Article  Google Scholar 

  5. Gunraj H, Wang L, Wong A (2020) COVIDNet-CT: a tailored deep convolutional neural network design for detection of covid-19 cases from chest ct images. Front Med. https://doi.org/10.3389/fmed.2020.608525

    Article  Google Scholar 

  6. Maghdid HS, Asaad AT, Ghafoor KZ, Sadiq AS, Mirjalili S, Khan MK (2021) Diagnosing covid-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms. In: multimodal image exploitation and learning 2021, vol 11734, p 117340. International society for optics and photonics

  7. Ghoshal B, Tucker A (2020) Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv . https://doi.org/10.48550/ARXIV.2003.10769

  8. Wang J-J, Wang L (2022) A cooperative memetic algorithm with feedback for the energy-aware distributed flow-shops with flexible assembly scheduling. Comput Ind Eng 168:108126

    Article  Google Scholar 

  9. Zhao F, Di S, Wang L (2022) A hyperheuristic with q-learning for the multiobjective energy-efficient distributed blocking flow shop scheduling problem. IEEE Transact Cybern

  10. Zhao F, Jiang T, Wang L (2022) A reinforcement learning driven cooperative meta-heuristic algorithm for energy-efficient distributed no-wait flow-shop scheduling with sequence-dependent setup time. IEEE Transact Ind Inf

  11. Cohen JP, Morrison P, Dao L (2020) Covid-19 image data collection. arXiv preprint arXiv:2003.11597

  12. Kaggle (2020) Kaggle’s chest X-ray images (Pneumonia) dataset. URL: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

  13. Arute F, Arya K, Babbush R, Bacon D, Bardin JC, Barends R, Biswas R, Boixo S, Brandao FG, Buell DA et al (2019) Quantum supremacy using a programmable superconducting processor. Nature 574(7779):505–510

    Article  CAS  PubMed  ADS  Google Scholar 

  14. Nivelkar M, Bhirud S (2022) Modeling of supervised machine learning using mechanism of quantum computing. J Phys Conf Ser 2161:012023 (IOP Publishing)

    Article  Google Scholar 

  15. Dou T, Zhang G, Cui W (2022) Efficient quantum feature extraction for CNN-based learning. arXiv preprint arXiv:2201.01246

  16. Chen G, Chen Q, Long S, Zhu W, Yuan Z, Wu Y (2022) Quantum convolutional neural network for image classification. Pattern Anal Appl, pp 1–13

  17. Choudhuri R, Halder A (2022) Brain mri tumour classification using quantum classical convolutional neural net architecture. Neural Comput Appl, pp 1–12

  18. Cohen JP, Morrison P, Dao L (2020) COVID-19 image data collection. arXiv e-prints, 2003–11597 arXiv:2003.11597 [eess.IV]

  19. Kaggle’s chest X-ray images (Pneumonia) dataset, (2020). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Accessed: 2022-09-30

  20. Tuberculosis (TB) Chest X-ray database. https://www.https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset. Accessed: 2022-09-30

  21. Powers DM (2020) Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061

  22. Actualmed COVID-19 chest X-ray dataset initiative. https://github.com/agchung/Actualmed-COVID-chestxray-dataset

  23. Cohen JP, Morrison P, Dao L (2020) COVID-19 image data collection. arXiv . https://doi.org/10.48550/ARXIV.2003.11597

  24. Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Anal Appl 24:1207–1220

    Article  PubMed  PubMed Central  Google Scholar 

  25. Loey M, Smarandache F, Khalifa MNE (2020) Within the lack of chest covid-19 x-ray dataset: a novel detection model based on gan and deep transfer learning. Symmetry 12(4):651

    Article  CAS  ADS  Google Scholar 

  26. Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D (2021) Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for covid-19. IEEE Rev Biomed Eng 14:4–15. https://doi.org/10.1109/RBME.2020.2987975

    Article  PubMed  Google Scholar 

  27. Oh Y, Park S, Ye JC (2020) Deep learning covid-19 features on cxr using limited training data sets. IEEE Transact Med Imaging 39(8):2688–2700

    Article  Google Scholar 

  28. Khan AI, Shah JL, Bhat MM (2020) Coronet: a deep neural network for detection and diagnosis of covid-19 from chest x-ray images. Comput Methods Progr Biomed 196:105581

    Article  Google Scholar 

  29. Minaee S, Kafieh R, Sonka M, Yazdani S, Soufi GJ (2020) Deep-COVID: Predicting covid-19 from chest x-ray images using deep transfer learning. Med Image Anal 65:101794

    Article  PubMed  PubMed Central  Google Scholar 

  30. Brunese L, Mercaldo F, Reginelli A, Santone A (2020) Explainable deep learning for pulmonary disease and coronavirus covid-19 detection from x-rays. Comput Methods Progr Biomed 196:105608

    Article  Google Scholar 

  31. Ullah Z, Usman M, Gwak J (2023) Mtss-aae: Multi-task semi-supervised adversarial autoencoding for covid-19 detection based on chest x-ray images. Exp Syst Appl 119475

  32. Wang L, Lin ZQ, Wong A (2020) Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Sci Rep 10(1):1–12

    CAS  Google Scholar 

  33. Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131

    Article  CAS  PubMed  Google Scholar 

  34. Babic D, Jovovic I, Popovic T, Cakic S, Filipovic L (2022) Detecting pneumonia with tensorflow and convolutional neural networks. In: 2022 IEEE international conference on omni-layer intelligent systems (COINS), pp 1–4 . https://doi.org/10.1109/COINS54846.2022.9854948

  35. Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R, Hamid T, Islam MT, Kashem S, Mahbub ZB, Ayari MA, Chowdhury MEH (2020) Reliable tuberculosis detection using chest x-ray with deep learning, segmentation and visualization. IEEE Access 8:191586–191601. https://doi.org/10.1109/ACCESS.2020.3031384

    Article  Google Scholar 

  36. Dey S, Roychoudhury R, Malakar S, Sarkar R (2022) An optimized fuzzy ensemble of convolutional neural networks for detecting tuberculosis from chest x-ray images. Appl Soft Comput 114:108094

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, UP, 211004, India

    Riya Arora, G. V. Eswara Rao, Shashwati Banerjea & Rajitha B.

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  1. Riya Arora
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  2. G. V. Eswara Rao
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  3. Shashwati Banerjea
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Correspondence to Shashwati Banerjea.

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Arora, R., Rao, G.V.E., Banerjea, S. et al. MLDC: multi-lung disease classification using quantum classifier and artificial neural networks. Neural Comput & Applic 36, 3803–3816 (2024). https://doi.org/10.1007/s00521-023-09207-3

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