453
Views
0
CrossRef citations to date
0
Altmetric
Research Article

RePoint-Net detection and 3DSqU² Net segmentation for automatic identification of pulmonary nodules in computed tomography images

, , &
Article: 2258998 | Received 28 May 2023, Accepted 10 Sep 2023, Published online: 30 Sep 2023

References

  • Al-Shabi M, Lee HK, Tan M. 2019. Gated-dilated networks for lung nodule classification in CT scans. IEEE Access. 7:178827–23. doi: 10.1109/ACCESS.2019.2958663.
  • Ayaz M, Shaukat F, Raja G. 2021. Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors. Phys Eng Sci Med. 44(1):183–194. doi: 10.1007/s13246-020-00966-0.
  • Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. 2018. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal For Clinicians. 68(6):394–424. doi: 10.3322/caac.21492.
  • Cao H, Liu H, Song E, Ma G, Xu X, Jin R, Liu T, Hung C-C. 2019. Multi-Branch Ensemble learning architecture based on 3D CNN for false positive reduction in lung nodule detection. IEEE Access. 7:67380–67391. doi: 10.1109/ACCESS.2019.2906116.
  • Chen G, Li S, KarakiChen G, Li S, Karakikes I, Ren L, Chow MZ, Chopra A, Keung W, Chen NIH, et al. 2015. Phospholamban as a crucial determinant of the inotropic response of human pluripotent stem cell-derived ventricular cardiomyocytes and engineered 3-dimensional tissue constructs. Circ Arrhythm Electrophysiol. doi: 10.1161/CIRCEP.114.002049.
  • Chollet F. 2017. Xception: deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. p. 1251–1258. doi: 10.48550/arXiv.1610.02357.
  • Desai AD. 2019. Technical considerations for semantic segmentation in MRI using convolutional neural networks. arXiv preprint arXiv:1902.01977.
  • Ding J. 2017. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham. doi: 10.1007/978-3-319-66179-7_64.
  • Ding J. 2017b. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In Medical Image Computing and Computer Assisted Intervention− MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20. Springer.
  • Ding X, Li Q, Cheng Y, Wang J, Bian W, Jie B. 2020. Local keypoint-based faster R-CNN. Appl Intell. 50(10):3007–3022. doi: 10.1007/s10489-020-01665-9.
  • Ding S, Wang H, Lu H, Nappi M, Wan S. 2022. Two path gland segmentation algorithm of colon pathological image based on local semantic guidance. IEEE J Biomed Health Inform. 27(4):1701–1708. doi: 10.1109/JBHI.2022.3207874.
  • Dou Q, Chen H, Yu L, Qin J, Heng P-A. 2017. Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng. 64(7):1558–1567. doi: 10.1109/TBME.2016.2613502.
  • El-Bana S, Al-Kabbany A, Sharkas M. 2020. A two-stage framework for automated malignant pulmonary nodule detection in CT scans. Diagnostics. 10(3):131. doi: 10.3390/diagnostics10030131.
  • Faster R. 2015. Towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst. 9199. doi: 10.48550/arXiv.1506.01497.
  • Goutte C and Gaussier E. 2005. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In European conference on information retrieval. Springer, Berlin, Heidelberg. p. 345–359. doi: 10.1007/978-3-540-31865-1_25 .
  • Gu Y, Lu X, Yang L, Zhang B, Yu D, Zhao Y, Gao L, Wu L, Zhou T. 2018. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs. Comput Biol Med. 103:220–231. doi: 10.1016/j.compbiomed.2018.10.011.
  • Han G, Liu X, Zhang H, Zheng G, Soomro NQ, Wang M, Liu W. 2019. Hybrid resampling and multi-feature fusion for automatic recognition of cavity imaging sign in lung CT. Future Gen Compt Syst. 99:558–570. doi: 10.1016/j.future.2019.05.009.
  • Harvey EC, Feng M, Ji X, Zhang R, Li Y, Chen G-H, Li K. 2019. Impacts of photon counting CT to maximum intensity projection (MIP) images of cerebral CT angiography: theoretical and experimental studies. Phys Med Biol. 64(18):185015. doi: 10.1088/1361-6560/ab32fe.
  • Hasan SM, Uddin MP, Mamun MA, Sharif MI, Ulhaq A, Krishnamoorthy G. 2022. A machine learning framework for early-stage detection of autism spectrum disorders. IEEE Access. 11:15038–15057. doi: 10.1109/ACCESS.2022.3232490.
  • He K. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. p. 770–778. http://image-net.org/challenges/LSVRC/2015.
  • Hinton GE. 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580.
  • Howard AG. 2017. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
  • Huang X, Sun W, Tseng TL(, Li C, Qian W. 2019. Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Comput Med Imaging Graphics. 74:25–36. doi: 10.1016/j.compmedimag.2019.02.003.
  • Hussain MA, et al. 2015. Lung cancer detection using artificial neural network & fuzzy clustering. Int J Adv Res Comput Commun Eng. 4(3):360–363. doi: 10.17148/IJARCCE.2015.4386.
  • Jaffe TA, Wickersham NW, Sullivan DC. 2010. Quantitative imaging in oncology patients: part 1, radiology practice patterns at major US cancer centers. AJR Am J Roentgenol. 195(1):101–106. doi: 10.2214/AJR.09.2850.
  • Jin H, Li Z, Tong R, Lin L. 2018. A deep 3D residual CNN for false‐positive reduction in pulmonary nodule detection. Med Phys. 45(5):2097–2107. doi: 10.1002/mp.12846.
  • Jun TJ. 2019. T-Net: encoder-decoder in encoder-decoder architecture for the main vessel segmentation in coronary angiography. arXiv preprint arXiv:1905.04197.
  • Khan MA, Rubab S, Kashif A, Sharif MI, Muhammad N, Shah JH, Zhang Y-D, Satapathy SC. 2020. Lungs cancer classification from CT images: an integrated design of contrast based classical features fusion and selection. Pattern Recognit Lett. 129:77–85. doi: 10.1016/j.patrec.2019.11.014.
  • Khan MA, Sharif MI, Raza M, Anjum A, Saba T, Shad SA. 2022. Skin lesion segmentation and classification: a unified framework of deep neural network features fusion and selection. Expert Syst. 39(7):e12497. doi: 10.1111/exsy.12497.
  • Khosravan N and Bagci U. 2018. S4ND: single-shot single-scale lung nodule detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention. : Springer, Cham. p. 794–802. doi: 10.1007/978-3-030-00934-2_88.
  • Kingma DP, Ba J. 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980.
  • Kong T. 2016. Hypernet: towards accurate region proposal generation and joint object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition; p. 845–853.
  • Lan T. 2018. Run: residual u-net for computer-aided detection of pulmonary nodules without candidate selection. arXiv preprint arXiv:1805.11856.
  • Liao F, Liang M, Li Z, Hu X, Song S. 2019. Evaluate the malignancy of pulmonary nodules using the 3-d deep leaky noisy-or network. IEEE Trans Neural Netw Learning Syst. 30(11):3484–3495. doi: 10.1109/TNNLS.2019.2892409.
  • Li W, Cao P, Zhao D, Wang J. 2016. Pulmonary nodule classification with deep convolutional neural networks on computed tomography images. Comput Math Method M. 2016:1–7. doi: 10.1155/2016/6215085.
  • Li B, Liu S, Wu F, Li G, Zhong M, Guan X. 2022. RT‐Unet: an advanced network based on residual network and transformer for medical image segmentation. Int J Intell Sys. 37(11):8565–8582. doi: 10.1002/int.22956.
  • Liu M, Zhou Z, Liu F, Wang M, Wang Y, Gao M, Sun H, Zhang X, Yang T, Ji L, et al. 2022. CT and CEA -based machine learning model for predicting malignant pulmonary nodules. Cancer Sci. 113(12):4363–4373. doi: 10.1111/cas.15561.
  • Lokhandwala T. 2017. Costs of diagnostic assessment for lung cancer: a medicare claims analysis. Clin Lung Cancer. 18(1):e27–e34. doi: 10.1016/j.cllc.2016.07.006.
  • Masood A, Yang P, Sheng B, Li H, Li P, Qin J, Lanfranchi V, Kim J, Feng DD. 2019. Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT. IEEE J Transl Eng Health Med. 8:1–13. doi: 10.1109/JTEHM.2019.2955458.
  • Men K, Boimel P, Janopaul-Naylor J, Zhong H, Huang M, Geng H, Cheng C, Fan Y, Plastaras JP, Ben-Josef E, et al. 2018. Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy. Phys Med Biol. 63(18):185016. doi: 10.1088/1361-6560/aada6c.
  • Morozov SP, Andreychenko AE, Blokhin IA, Gelezhe PB, Gonchar AP, Nikolaev AE, Pavlov NA, Chernina VY, Gombolevskiy VA. 2020. MosMedData: data set of 1110 chest CT scans performed during the COVID-19 epidemic. Digital Diagn. 1(1):49–59. doi: 10.17816/DD46826.
  • Mou L, Hua Y, Jin P, Zhu XX. 2020. ERA: a data set and deep learning benchmark for event recognition in aerial videos [software and data sets]. IEEE Geosci Remote Sens Mag. 8(4):125–133. doi: 10.1109/MGRS.2020.3005751.
  • Nguyen N-Q, Lee S-W. 2019. Robust boundary segmentation in medical images using a consecutive deep encoder-decoder network. IEEE Access. 7:33795–33808. doi: 10.1109/ACCESS.2019.2904094.
  • Ni B, Liu Z, Cai X, Nappi M, Wan S. 2023. Segmentation of ultrasound image sequences by combing a novel deep siamese network with a deformable contour model. Neural Comput Applic. 35(20):14535–14549. doi: 10.1007/s00521-022-07054-2.
  • Rahman MA and Wang Y. 2016. Optimizing intersection-over-union in deep neural networks for image segmentation. In International symposium on visual computing. Springer, Cham. p. 234–244. doi: 10.1007/978-3-319-50835-1_22.
  • Ren S, He K, Girshick R, Sun J. 2017. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 39(6):1137–1149. doi: 10.1109/TPAMI.2016.2577031.
  • Rippel O, Snoek J, Adams RP. 2015. Spectral representations for convolutional neural networks. Adv Neural Inf Process Syst. 28:2449–2457.
  • Setio AAA, Traverso A, de Bel T, Berens MSN, Bogaard CVD, Cerello P, Chen H, Dou Q, Fantacci ME, Geurts B, et al. 2017. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Med Image Anal. 42:1–13. doi: 10.1016/j.media.2017.06.015.
  • Sharif MI. 2021. A decision support system for multimodal brain tumor classification using deep learning. Complex & Intelligent Systems. 1–14. doi: 10.1007/s40747-021-00321-0.
  • Shih K-H. 2019. Real-time object detection with reduced region proposal network via multi-feature concatenation. In IEEE transactions on neural networks and learning systems. p. 2164–2173. doi: 10.1109/TNNLS.2019.2929059.
  • Shi L, Ma H, Zhang J. 2020. Automatic detection of pulmonary nodules in CT images based on 3D res-I network. Vis Comput. 37(6):1–14. doi: 10.1007/s00371-020-01869-7.
  • Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Singh SP, Wang L, Gupta S, Goli H, Padmanabhan P, Gulyás B. 2020. 3D deep learning on medical images: a review. Sensors. 20(18):5097. doi: 10.3390/s20185097.
  • Srivastava N. 2014. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 15(1):1929–1958.
  • Sun W, Zheng B, Qian W. 2017. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med. 89:530–539. doi:10.1016/j.compbiomed.2017.04.006.
  • Tran GS, Nghiem TP, Nguyen VT, Luong CM, Burie J-C. 2019. Improving accuracy of lung nodule classification using deep learning with focal loss. J Healthc Eng. 2019:1–9. doi: 10.1155/2019/5156416.
  • Uijlings JR, van de Sande KEA, Gevers T, Smeulders AWM. 2013. Selective search for object recognition. Int J Comput Vis. 104(2):154–171. doi: 10.1007/s11263-013-0620-5.
  • Umer MJ, Sharif MI. 2022. A comprehensive survey on quantum machine learning and possible applications. Int J E-Health Med Commun. 13(5):1–17. doi: 10.4018/IJEHMC.315730.
  • Wang S-H, Fernandes SL, Zhu Z, Zhang Y-D. 2021. AVNC: attention-based VGG-style network for COVID-19 diagnosis by CBAM. IEEE Sensors J. 22(18):17431–17438. doi: 10.1109/JSEN.2021.3062442.
  • Wang S, Yang DM, Rong R, Zhan X, Fujimoto J, Liu H, Minna J, Wistuba II, Xie Y, Xiao G, et al. 2019. Artificial intelligence in lung cancer pathology image analysis. Cancers. 11(11):1673. doi:10.3390/cancers11111673.
  • Wei J. 2019. P3D-CTN: pseudo-3D convolutional tube network for spatio-temporal action detection in videos. In 2019 IEEE International Conference on Image Processing (ICIP); 22-25 September 2019; IEEE. doi: 10.1109/ICIP.2019.8802979.
  • Wu Y. 2023. Cdt-cad: context-aware deformable transformers for end-to-end chest abnormality detection on x-ray images. IEEE/ACM Transactions on Computational Biology and Bioinformatics. doi: 10.1109/tcbb.2023.3258455.
  • Wu Z, Su L, and Huang Q. 2019. Cascaded partial decoder for fast and accurate salient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. doi: 10.48550/arXiv.1904.08739.
  • Xie H, Yang D, Sun N, Chen Z, Zhang Y. 2019. Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recognit. 85:109–119. doi: 10.1016/j.patcog.2018.07.031.
  • Zafar M, Sharif MI, Sharif MI, Kadry S, Bukhari SAC, Rauf HT. 2023. Skin lesion analysis and cancer detection based on machine/deep learning techniques: a comprehensive survey. Life. 13(1):146. doi: 10.3390/life13010146.
  • Zagoruyko S and Komodakis N. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146.
  • Zhang G. 2018. Spatial pyramid dilated network for pulmonary nodule malignancy classification. In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE.
  • Zhang Y, Attique Khan M, Zhu Z, Wang S. 2023. SNELM: SqueezeNet-guided ELM for COVID-19 recognition. Comput Syst Sci Eng. 46(1):13. doi: 10.32604/csse.2023.034172.
  • Zhang H, Peng Y, Guo Y. 2022. Pulmonary nodules detection based on multi-scale attention networks. Sci Rep. 12(1):1–14. doi: 10.1038/s41598-022-05372-y.
  • Zhao C. 2018. Lung nodule detection via 3D U-Net and contextual convolutional neural network. In 2018 International Conference on Networking and Network Applications (NaNA); Xi'an, China; IEEE. p. 356–361. doi: 10.1109/NANA.2018.8648753.
  • Zhong Z, Sun L, Huo Q. 2019. An anchor-free region proposal network for faster R-CNN-based text detection approaches. Int J Doc Anal Recogn. 22(3):315–327. doi: 10.1007/s10032-019-00335-y.
  • Zhou H, Jin Y, Dai L, Zhang M, Qiu Y, Wang K, Tian J, Zheng J. 2020. Differential diagnosis of benign and malignant thyroid nodules using deep learning radiomics of thyroid ultrasound images. Eur J Radiol. 127:108992. doi: 10.1016/j.ejrad.2020.108992.
  • Zhu W. 2018a. Deepem: deep 3d convnets with em for weakly supervised pulmonary nodule detection. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer. doi: 10.1007/978-3-030-00934-2_90.
  • Zhu W. 2018b. Deeplung: deep 3d dual path nets for automated pulmonary nodule detection and classification. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV); 12–15 March 2018; Lake Tahoe, NV, USA: IEEE. doi: 10.1109/WACV.2018.00079.