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LCD-Capsule Network for the Detection and Classification of Lung Cancer on Computed Tomography Images

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Abstract

Lung cancer is the second most prominent cancer in men and women, and it is also the leading cause of cancer-related mortality.If lung cancer is diagnosed early, when it is minuscule and has not spread, it is preferable to be adequately treated. A non-invasive low-dose Computed Tomography (CT) scan can detect abnormal patches in the lungs that could be cancerous. It is proposed that machine learning and pattern classification be used to identify and categorize lung cancer from CT scans. Pattern classification algorithms like deep learning can categorize input data into various classes based on the input’s characteristic features. A novel deep learning framework formulated by the encapsulation of a Convolutional Neural Network (CNN) and a Capsule Neural Network (CapsNet) called LCD-CapsNet, leveraging the capabilities of these networks to minimize vast amounts of data and achieve spatial invariance for lung cancer detection and classification using CT images is proposed. The primary objective of the proposed method is to create algorithms that would classify and examine images from a dataset to determine whether or not a patient had or posed a danger of developing lung cancer. The Lung Image Database Consortium (LIDC) datasets are utilized to assess this deep learning model, from which 4335 images were collected for the training and testing pipeline. The results demonstrated that LCD-CapsNet outperforms CapsNet, with an average Precision of 95 %, Recall of 94.5 %, F1-Score 94.5 %, Specificity 99.07 %, Area Under the Curve of 0.989, and Accuracy 94 % of benign and malignant data.

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Data Availability

LIDC-IDRI data are publicly availabe

References

  1. Abubeker KM, Baskar S (2022) A Machine Learning Strategy for Internet-of-Things-Enabled Diabetic Prediction to Mitigate Pneumonia Risk. In: In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (pp. 1-6). IEEE

  2. Abubeker KM, Baskar S (2022) Wireless sensor and wireless body area network assisted biosensor network for effective monitoring and prevention of non-ventilator hospital-acquired pneumonia. Frontiers in Sustainable Cities 4

  3. Adebisi OA, Ajagbe SA, Ojo JA, Oladipupo MA (2022) Computer techniques for medical image classification: a review. Intelligent Healthcare, pp 19–36

  4. Adu K, Yu Y, Cai J, Owusu-Agyemang K, Twumasi BA, Wang X (2021) DHS-Capsnet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images. Int J Imaging Syst Technol 31(4):2075–2092

    Article  Google Scholar 

  5. Afshar P, Naderkhani F, Oikonomou A, Rafiee MJ, Mohammadi A, Plataniotis KN (2021) MIXCAPS: A capsule network-based mixture of experts for lung nodule malignancy prediction. Pattern Recognit 116:107942

    Article  Google Scholar 

  6. Afshar P, et al. (2020) 3D-MCN: A 3D Multi-scale Capsule Network for Lung Nodule Malignancy Prediction. Sci Rep 10 (1):7948. https://doi.org/10.1038/s41598-020-64824-5

    Article  MathSciNet  Google Scholar 

  7. Ajagbe SA, Oki OA, Oladipupo MA, Nwanakwaugwum A (2022) Investigating the efficiency of deep learning models in bioinspired object detection. In: 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) (pp. 1-6). IEEE

  8. Al-Huseiny MS, Sajit AS (2021) Transfer learning with GoogLeNet for detection of lung cancer. Indones J Electr Eng Comput Sci 22(2):1078. https://doi.org/10.11591/ijeecs.v22.i2.pp1078-1086

    Article  Google Scholar 

  9. Al-Yasriy HF, Al-Husieny MS, Mohsen FY, Khalil EA, Hassan ZS (2020) Diagnosis of lung cancer based on CT scans using CNN. IOP conf. Ser Mater Sci Eng 928(2)

  10. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Ye D, Al-Shamma O, Santamaría J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8:53

    Article  Google Scholar 

  11. Armato SG, et al. (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans. Med Phys 38(2):915–931

    Article  Google Scholar 

  12. Asuntha A, Srinivasan (2020) A Deep learning for lung Cancer detection and classification. Multimed Tools Appl 79:7731–7762

    Article  Google Scholar 

  13. Avanzo M, Stancanello J, Pirrone G, Sartor G (2020) Radiomics and deep learning in lung cancer. Strahlentherapie und Onkol 196(10):879–887

    Article  Google Scholar 

  14. Aydın N, Çelik Ö, Aslan AF, Odabaş A, Dündar E, Şahin MC (2021) Detection of lung cancer on computed tomography using artificial intelligence applications developed by deep learning methods and the contribution of deep learning to the classification of lung carcinoma. Current Medical Imaging 17 (9):1137–1141

    Article  Google Scholar 

  15. Bhandary A, Ananth Prabhu G, Rajinikanth V, Palani Thanaraj K, Satapathy SC, Robbins DE, Shasky Charles, Zhang Yu-Dong, Tavares JMRS, Sri Madhava Raja N (2020) Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images. Pattern Recognit Lett 129:271–278

    Article  Google Scholar 

  16. Bushara AR, Vinod Kumar RS (2022) A deep learning-based lung cancer classification of CT images using augmented convolutional neural networks, Electronic Letters on Computer Vision and Image Analysis 21(1). https://doi.org/10.5565/rev/elcvia.1490

  17. Causey JL, Zhang J, Ma S, et al. (2018) Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep 8(1):1–12

    Article  Google Scholar 

  18. Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4-5):198–211

    Article  Google Scholar 

  19. El-Askary NS, Salem MAM, Roushdy MI (2019) Feature extraction and analysis for lung nodule classification using random forest. In: Inproceedings of the 2019 8th International Conference on Software and Information Engineering, pp 248–252

  20. Ghani T, Oommen JB (2021) On utilizing 2D features from 3D scans to enhance the prediction of lung cancer survival rates. Pattern Recognit Lett 152:56–62

    Article  Google Scholar 

  21. Guo Z, Xu L, Si Y, Razmjooy N (2021) Novel computer-aided lung cancer detection based on convolutional neural network-based and feature-based classifiers using metaheuristics. Int J Imaging Syst Technol 31(4):1954–1969. https://doi.org/10.1002/ima.22608

    Article  Google Scholar 

  22. Huidrom R, Chanu YJ, Singh KM (2022) Neuro-evolutional based computer aided detection system on computed tomography for the early detection of lung cancer. Multimed Tools Appl 81:32661–32673

    Article  Google Scholar 

  23. Jain S, Salau AO (2019) An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins. Cogent Eng 6 (1):1599537

    Article  Google Scholar 

  24. Kalaivani N, Manimaran N, Sophia S, Devi DD (2020) Deep learning based lung cancer detection and classification, IOP conf. Ser Mater Sci Eng 994(1). https://doi.org/10.1088/1757-899X/994/1/012026

  25. Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53 (8):5455–5516

    Article  Google Scholar 

  26. Koresh HJD, Chacko S, Periyanayagi M (2021) A modified capsule network algorithm for oct corneal image segmentation. Pattern Recognit Lett 143:104–112

    Article  Google Scholar 

  27. Kumar SS, Nithila EE (2016) Lung cancer diagnosis from CT images using CAD system: a review. Int J Biomed Eng Technol 21(4):311–321

    Article  Google Scholar 

  28. Lennartz S, et al. (2021) Texture analysis of iodine maps and conventional images for k-nearest neighbor classification of benign and metastatic lung nodules. Cancer Imaging 21(1):1–10

    Article  MathSciNet  Google Scholar 

  29. Makaju S, Prasad PWC, Alsadoon A, Singh AK, Elchouemi A (2018) Lung cancer detection using CT scan Images. Procedia Computer Science 125:1877–0509

    Article  Google Scholar 

  30. Manju BR, Athira V, Rajendran A (2021) Efficient multi-level lung cancer prediction model using support vector machine classifier. In: IOP Conference Series: Materials Science and Engineering (Vol. 1012, No. 1, p. 012034). IOP Publishing

  31. Mobiny A, Van Nguyen H (2018) Fast CapsNet for Lung Cancer Screening. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11071. Springer, New York

  32. Mobiny A, et al. (2021) Memory-Augmented Capsule network for adaptable lung nodule classification. IEEE Trans Med Imaging:1–11. https://doi.org/10.1109/TMI.2021.3051089

  33. Naqi SM, Sharif M, Lali IU (2019) A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection. Multimed Tools Appl 78:26287–26311

    Article  Google Scholar 

  34. Nasser IM, Abu-Naser SS (2019) Lung cancer detection using artificial neural network. International Journal of Engineering and Information Systems (IJEAIS) 3(3):17–23

    Google Scholar 

  35. Nithila EE, Kumar SS (2016) Segmentation Of lung nodule in CT data using active contour model and Fuzzy C-mean clustering. Alex Eng J 55(3):2583–2588

    Article  Google Scholar 

  36. Nithila EE, Kumar SS (2017) Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images. Eng Sci Technol Int J 20(3):1192–202

    Google Scholar 

  37. Nithila EE, Kumar S (2019) Segmentation of lung from CT using various active contour models. Biomed Signal Process Control 47:57–62

    Article  Google Scholar 

  38. Olalekan A, Jain S (2021) Informatics in Medicine Unlocked Adaptive diagnostic machine learning technique for classification of cell decisions for AKT protein. Informatics Med Unlocked 23:100511. https://doi.org/10.1016/j.imu.2021.100511

    Article  Google Scholar 

  39. Pang S, Zhang Y, Ding M, Wang X, Xie X (2020) A deep model for lung cancer type identification by densely connected convolutional networks and adaptive boosting. IEEE Access 8:4799–4805

    Article  Google Scholar 

  40. Parveen SS, Kavitha C (2014) Classification of lung cancer nodules using SVM Kernels. Int J Comput Appl 95(25)

  41. Phani Kumar M, Dutta S, Murmu NC (2021) Tool wear classification based on machined surface images using convolution neural networks. Sādhanā 46:130

    Article  Google Scholar 

  42. Polat H, Mehr HD (2019) Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture. Appl Sci 9(5)

  43. Ramana K, Kumar MR, Sreenivasulu K, Gadekallu TR (2022) Early prediction of lung cancers using deep saliency capsule and Pre-Trained deep learning frameworks. Front Oncol 12(June):1–13. 10.3389/fonc.2022.886739

    Article  Google Scholar 

  44. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. Adv Neural Inf Process Syst,pp 3856–3866

  45. Salama WM, Shokry A, Aly MHA (2022) Generalized framework for lung Cancer classification based on deep generative models. Multimed Tools Appl 81:32705–32722

    Article  Google Scholar 

  46. Salau AO, Min M (2019) Feature extraction: A survey of the types, techniques, applications. In: 5th IEEE International Conference on Signal Processing and Communication (ICSC), Noida, India, pp158–164. https://doi.org/10.1109/ICSC45622.2019.8938371

  47. Saleh AY, Chin CK, Penshie V, Al-Absi HRH (2021) Lung cancer medical images classification using hybrid cnn-svm. Int J Adv Intell Informatics 7 (2):151–162. https://doi.org/10.26555/ijain.v7i2.317

    Article  Google Scholar 

  48. Shafi I, et al. (2022) An effective method for lung cancer diagnosis from CT scan using deep Learning-Based support vector network. Cancers (Basel) 14 (5457):1–18

    Google Scholar 

  49. Shen W, Zhou M, Yang F, Dongdong Y, Di D, Yang C, Zang Y, Tian J (2017) Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification. Pattern Recognit 61:663–673

    Article  Google Scholar 

  50. Siegel RL, Miller KD, Fuchs HE, Jemal A (2021) Cancer statistics. CA Cancer J Clin 71(1):7–33

    Article  Google Scholar 

  51. Song QZ, Zhao L, Luo XK, Dou XC (2017) Using deep learning for classification of lung nodules on computed tomography images. J Healthcare Eng 2017(8314740):7

    Google Scholar 

  52. Sori WJ, Feng J, Godana AW, Liu S, Gelmecha DJ (2021) DFD-Net: lung cancer detection from denoised CT scan image using deep learning. Front Comput Sci 15(2)

  53. Tang B, Li A, Li B, Wang M (2019) Capsurv: Capsule network for survival analysis with whole slide pathological images. IEEE Access 7:26022–26030

    Article  Google Scholar 

  54. Thakur SK, Singh DP, Choudhary J (2020) mLung cancer identification: a review on detection and classification. Cancer Metastasis Rev 39(3):989–998

    Article  Google Scholar 

  55. Wu P, Sun X, Zhao Z, Wang H, Pan S, Schuller B (2020) Classification of lung nodules based on deep residual networks and migration learning. Comput Intell Neurosci, vol 2020

  56. Yu H, Li J, Zhang L, Cao Y, Yu X, Sun J (2021) Design of lung nodules segmentation and recognition algorithm based on deep learning. BMC Bioinforma 22(314):1–20. https://doi.org/10.1186/s12859-021-04234-0

    Article  Google Scholar 

  57. Yu H, Zhou Z, Wang Q (2020) Deep learning assisted predict of lung cancer on computed tomography images using the adaptive hierarchical heuristic mathematical model”. IEEE Access 8:86400–86410

    Article  Google Scholar 

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Acknowledgment

The authors acknowledge the National Cancer Institute and the National Institutes of Health for their contributions to the development of the LIDC/IDRI database, which is free and open to the public. There was no specific grant awarded for this study by any government, commercial, or nonprofit organization.

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    1. Department of Electronics and Communication Engineering, Noorul Islam Center for Higher Education, Kumarakoil, Kanyakumari, 629180, Tamil Nadu, India

      Bushara A.R. & Vinod Kumar R.S.

    2. Department of Electronics and Instrumentation Engineering, Noorul Islam Center for Higher Education, Kumarakoil, Kanyakumari, 629180, Tamil Nadu, India

      Kumar S.S.

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    A.R., B., R.S., V.K. & S.S., K. LCD-Capsule Network for the Detection and Classification of Lung Cancer on Computed Tomography Images. Multimed Tools Appl 82, 37573–37592 (2023). https://doi.org/10.1007/s11042-023-14893-1

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