Abstract
Lung cancer is often a fatal disease. To minimize patient mortality, the ability to identify the nodule malignancy stage from computed tomography (CT) lung scans is critical. Most existing methods rely exclusively on deep learning (DL) networks. However, duplicated structures and insufficient training data make DL-based malignancy diagnosis from CT images time-consuming and imprecise. Additionally, the features that DL networks use to make decisions cannot be recognized. In response to these challenges in previous work, we built an end-to-end conventional machine learning (ML) model called ‘NoduleDiag’ to classify cancerous CT images and their malignancy stage based on the nodule characteristics observable in CT images that radiologists typically prefer for diagnosis. We also prepared a fully automatic pulmonary nodule segmentation model for implementation during the final stage of diagnosis (after disease-stage identification) and added it to our classification framework as immense practical assistance to radiologists and medical personnel. To the best of our knowledge, this work will be the first all in one computer-assisted diagnosis (CAD) framework with lung cancer classification, automatic malignancy-stage identification and nodule segmentation screening together using conventional ML approaches. Our proposed model classifies nodule malignancy on a scale of 1–5. It achieves 99.65% accuracy, 99.64% sensitivity, 99.76% precision, 99.9% specificity, and 99.91% negative predictive value (NPV) with XGBoost. Our segmentation model uses DeepLabv3+ (weights initialized by ResNet-18), achieving a 99.86% global accuracy, mean boundary F1 (BF) score of 0.976, 0.715 mean IoU and 0.997 weighted IoU.
Similar content being viewed by others
Data availability
The [22] repository contains the database utilized in the current investigation.
References
Chen X, Duan Q, Wu R, Yang Z (2021) Segmentation of lung computed tomography images based on SegNet in the diagnosis of lung cancer. J Radiat Res Appl Sci 14(1):396–403. https://doi.org/10.1080/16878507.2021.1981753
“Cancer (n.d.)” https://www.who.int/news-room/fact-sheets/detail/cancer (accessed Mar. 20, 2022)
Somsunun K, Prapamontol T, Pothirat C, Liwsrisakun C, Pongnikorn D, Fongmoon D, Chantara S, Wongpoomchai R, Naksen W, Autsavapromporn N, Tokonami S (2022) Estimation of lung cancer deaths attributable to indoor radon exposure in upper northern Thailand. Sci Rep 12(1):1–10. https://doi.org/10.1038/s41598-022-09122-y
Mbeje NP, Ginindza T, Jafta N (2022) Epidemiological Study of Risk Factors for Lung Cancer in KwaZulu-Natal, South Africa. Int J Environ Res Pub Health 19(11):6752. https://doi.org/10.3390/IJERPH19116752
Goncalves S, Fong P-C, Blokhina M (2022) Artificial intelligence for early diagnosis of lung cancer through incidental nodule detection in low- and middle-income countries-acceleration during the COVID-19 pandemic but here to stay,” Am J Cancer Res, vol. 12, no. 1, p. 1. Accessed: Oct. 18, 2022. [Online]. Available:/pmc/articles/PMC8822269/
Maharjan N, Thapa N, Tu J (2020) Blood-based Biomarkers for Early Diagnosis of Lung Cancer: A Review Article. JNMA J Nepal Med Assoc 58(227):519. https://doi.org/10.31729/JNMA.5023
Luo Z, Brubaker MA, Brudno M (2017) Size & texture-based classification of lung tumors with 3D CNNs. Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017, pp. 806–814. https://doi.org/10.1109/WACV.2017.95
da Nóbrega RVM, Rebouças Filho PP, Rodrigues MB, da Silva SPP, Dourado Júnior CMJM, de Albuquerque VHC (2018) Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks. Neural Comput Appl 32(15):11065–11082. https://doi.org/10.1007/S00521-018-3895-1
Sathyan H, Panicker JV (2018) Lung Nodule Classification Using Deep ConvNets on CT Images. 2018 9th international conference on computing, communication and networking technologies, ICCCNT 2018. https://doi.org/10.1109/ICCCNT.2018.8494084
Bruntha PM et al (2021) Lung Nodule Classification using Shallow CNNs and Deep Transfer Learning CNNs. 2021 7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021, pp. 1474–1478. https://doi.org/10.1109/ICACCS51430.2021.9441702
Naik A, Edla DR, Dharavath R (2021) A deep feature concatenation approach for lung nodule classification. Lecture Notes Netw Syst 256:213–226. https://doi.org/10.1007/978-3-030-82469-3_19
Shaffie A et al. A novel framework for accurate and noninvasive pulmonary nodule diagnosis by integrating texture and contour descriptors. Proceedings - International Symposium on Biomedical Imaging, vol. 2021-April, pp. 1883–1886, 2021. https://doi.org/10.1109/ISBI48211.2021.9433830
Halder A, Chatterjee S, Dey D (2022) Adaptive morphology aided 2-pathway convolutional neural network for lung nodule classification. Biomed Signal Proc Contr 72:103347. https://doi.org/10.1016/J.BSPC.2021.103347
Agnes SA, Immanuel Alex PS, Anitha J, Arun Solomon A (2021) Classification of Lung nodules using Convolutional long short-term Neural Network. Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021. pp. 1349–1353. https://doi.org/10.1109/ICCMC51019.2021.9418319
Huang X, Lei Q, Xie T, Zhang Y, Hu Z, Zhou Q (2020) Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images. Knowl-Based Syst 204:106230. https://doi.org/10.1016/J.KNOSYS.2020.106230
Heuvelmans MA, van Ooijen PMA, Ather S, Silva CF, Han D, Heussel CP, Hickes W, Kauczor HU, Novotny P, Peschl H, Rook M, Rubtsov R, von Stackelberg O, Tsakok MT, Arteta C, Declerck J, Kadir T, Pickup L, Gleeson F, Oudkerk M (2021) Lung cancer prediction by deep learning to identify benign lung nodules. Lung Cancer 154:1–4. https://doi.org/10.1016/J.LUNGCAN.2021.01.027
Suresh S, Mohan S (2019) NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/J.JKSUCI.2019.11.013
Ali I, Muzammil M, Haq IU, Khaliq AA, Abdullah S (2020) Efficient lung nodule classification using transferable texture convolutional neural network. IEEE Access 8:175859–175870. https://doi.org/10.1109/ACCESS.2020.3026080
Chen Y, Wang Y, Hu F, Feng L, Zhou T, Zheng C (2021) Ldnnet: toward robust classification of lung nodule and cancer using lung dense neural network. IEEE Access 9:50301–50320. https://doi.org/10.1109/ACCESS.2021.3068896
Dang T, Nguyen TT, McCall J, Elyan E, Moreno-García CF (2021) Two layer Ensemble of Deep Learning Models for Medical Image Segmentation. https://doi.org/10.48550/arxiv.2104.04809
L. Nanni, D. Cuza, A. Lumini, A. Loreggia, S. Brahnam (2021) Deep ensembles in bioimage segmentation. https://doi.org/10.48550/arxiv.2112.12955
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. https://doi.org/10.1118/1.3528204
“GitHub - notmatthancock/pylidc: An object relational mapping for the LIDC dataset using sqlalchemy.” (n.d.) https://github.com/notmatthancock/pylidc (accessed Mar. 20, 2022)
Alshayeji M, Al-Buloushi J, Ashkanani A, Abed S (2021) Enhanced brain tumor classification using an optimized multilayered convolutional neural network architecture. Multimed Tools Appl 80(19):28897–28917. https://doi.org/10.1007/s11042-021-10927-8
Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11211 LNCS. pp. 833–851. https://doi.org/10.1007/978-3-030-01234-2_49
Alshayeji MH, Ellethy H, Abed S, Gupta R (2022) Computer-aided detection of breast cancer on the Wisconsin dataset: an artificial neural networks approach. Biomed Signal Proc Contr 71:103141. https://doi.org/10.1016/J.BSPC.2021.103141
Raghu S, Sriraam N, Temel Y, Rao SV, Kubben PL (2020) EEG based multiclass seizure type classification using convolutional neural network and transfer learning. Neural Netw 124:202–212. https://doi.org/10.1016/J.NEUNET.2020.01.017
Jaju S, Chandak M (2022) A transfer learning model based on ResNet-50 for flower detection. Proc - Internat Conf Appl Artificial Intel Comput, ICAAIC 2022:307–311. https://doi.org/10.1109/ICAAIC53929.2022.9792697
Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386
Tan M, Le QV (2019) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 36th International Conference on Machine Learning, ICML 2019, vol. 2019-June, pp. 10691–10700. https://doi.org/10.48550/arxiv.1905.11946
Majidpourkhoei R, Alilou M, Majidzadeh K, Babazadehsangar A (2021) A novel deep learning framework for lung nodule detection in 3d CT images. Multimed Tools Appl 80(20):30539–30555. https://doi.org/10.1007/S11042-021-11066-W
Suresh S, Mohan S (2020) ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis. Neural Comput Applic 32(20):15989–16009. https://doi.org/10.1007/S00521-020-04787-W
Jena SR, George ST, Ponraj DN (2021) Lung cancer detection and classification with DGMM-RBCNN technique. Neural Comput Appl 33(22):15601–15617. https://doi.org/10.1007/S00521-021-06182-5
Kasinathan G, Jayakumar S (2022) Cloud-based lung tumor detection and stage classification using deep learning techniques. Biomed Res Int, vol 2022:1–17. https://doi.org/10.1155/2022/4185835
Joshi S et al (2022) Analysis of smart lung tumor detector and stage classifier using deep learning techniques with internet of things. https://doi.org/10.1155/2022/4608145
Author information
Authors and Affiliations
Contributions
Mohammad H. Alshayeji – methodology, conceptualization, formal analysis, writing – original draft, review and editing, validation, software, visualization, and supervision.
Sa’ed. Abed- investigation, formal analysis, writing - original draft, review, editing and validation.
Corresponding author
Ethics declarations
Competing interests
No conflicts of interest are reported by the authors.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Alshayeji, M.H., Abed, S. Lung cancer classification and identification framework with automatic nodule segmentation screening using machine learning. Appl Intell 53, 19724–19741 (2023). https://doi.org/10.1007/s10489-023-04552-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-023-04552-1