Analysis on grading of lung nodule images with segmentation using u-net and classification with Convolutional Neural Network Fish Swarm Optimization

• Autorzy: Sudha R. , Uma Maheswari K.M.

Identyfikatory

DOI

10.1016/j.bbe.2024.12.002

• Języki publikacji: EN

Abstrakty

EN

Lung malignant tumors are abnormal growths of cells in the lungs that have the potential to invade nearby tissues and spread to other parts of the body. Early detection of these malignant lung tumors is crucial to avoid complications and improve patient outcomes. However, manual processing consumes time and is a tedious process. This might result in poor estimation on cancer-prognosis, leading the patients into a higher risk of mortality. Many existing literatures have detected the malignant tumors, yet, found certain difficulties with the identification of size, appearance and spread of cancerous-cells in lung region to determine how far it has been occupied. Hence, the present study aims to overcome the existing complications through Deep Learning based Swarm Intelligence Algorithms. Implementation of the proposed work is involved with three stages such as preprocessing, segmentation and classification. Besides, CT scan possess the capability for giving a comprehensive view than X-rays. Data are collected from LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) with lung CT-images and accomplishes pre-processing by removing noise efficiently using wiener filter. Further, changes in soft tissues of lungs are identified and segmented in the subsequent phase using U-Net and finally classification is performed using CFSO (Convolutional Neural Network Fish Swarm Optimization) to overcome the slight chance of misclassification error as proposed CFSO can lead to more efficient computational processes since FSO algorithms are designed to minimize computational costs while maximizing performance through their metaheuristic nature. This efficiency is particularly beneficial when dealing with large datasets typical in medical imaging, allowing faster processing times without sacrificing accuracy. Hence, amalgamation of CFSO can reduce the number of features, thus speeding up training and inference times. Through the performance assessment, IoU (Intersection over Union) value attained through the analysis is found to be 0.7822. Further, accuracy obtained by the proposed model is 97.80%, recall is 98.49%, precision is 96.8% and F1-score is 97.32%. Findings of the study exhibits the purposefulness of the study in clinical settings by potentially reducing false negatives in lung cancer screening, ultimately improving patient survival rates through earlier detection and treatment.

Słowa kluczowe

EN

image segmentation; image classification; U-Net; convolutional neural network; fish swarm optimization

PL

segmentacja obrazu; klasyfikacja obrazu; U-Net; sieć neuronowa konwolucyjna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2025
• Tom: Vol. 45, iss. 1
• Strony: 90--104
• Opis fizyczny: Bibliogr. 43 poz., rys., tab., wykr.

Twórcy

autor

Sudha R.

• Department of Computer Science and Engineering, School of computing, College of engineering and technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

autor

Uma Maheswari K.M.

• Department of Computing Technologies, School of Computing, College of engineering and technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

Bibliografia

• [1] Shi W, Cheng Y, Zhu H, Zhao L. Metabolomics and lipidomics in non-small cell lung cancer. Clin Chim Acta 2024. https://doi.org/10.1016/j.cca.2024.117823.
• [2] Agnes SA, Solomon AA, Karthick K. Wavelet U-Net++ for accurate lung nodule segmentation in CT scans: improving early detection and diagnosis of lung cancer. Biomed Signal Process Control 2024;87:105509. https://doi.org/10.1016/j. Bspc.2023.105509.
• [3] Gupta H, Singh H, Kumar A. Texture and radiomics inspired data-driven cancerous lung nodules severity classification. Biomed Signal Process Control 2024;88:105543. https://doi.org/10.1016/j.bspc.2023.105543.
• [4] UrRehman Z, Qiang Y, Wang L, Shi Y, Yang Q, Khattak SU, et al. Effective lung nodule detection using deep CNN with dual attention mechanisms. Sci Rep 2024;14 (1):3934. https://doi.org/10.1038/s41598-024-51833-x.
• [5] Sivakumar M, Chinnasamy S, Thanabal MS. An efficient combined intelligent system for segmentation and classification of lung cancer computed tomography images. PeerJ Comput Sci 2024;10:e1802.
• [6] Guo L, Zhou C, Xu J, et al. Deep learning for chest X-ray diagnosis: competition between radiologists with or without artificial intelligence assistance. J Digit Imaging Inform med 2024;37:922-34. https://doi.org/10.1007/s10278-024-00990-6.
• [7] Gugulothu VK, Balaji S. An early prediction and classification of lung nodule diagnosis on CT images based on hybrid deep learning techniques. Multimed Tools Appl 2023;1. https://doi.org/10.1007/s11042-023-15802-2.
• [8] Srivastava S, Jayaswal N, Kumar S, Sharma PK, Behl T, Khalid A, et al. Unveiling the potential of proteomic and genetic signatures for precision therapeutics in lung cancer management. Cell Signal 2024;113:110932. https://doi.org/10.1016/j. Cellsig.2023.110932.
• [9] Modak S, Abdel-Raheem E, Rueda L. Applications of deep learning in disease diagnosis of chest radiographs: a survey on materials and methods. Biomed Eng Adv 2023;5:100076. https://doi.org/10.1016/j.bea.2023.100076.
• [10] Saravanaprasad P, Karuppusamy SA. Advanced lung tumor diagnosis using a 3D deep neural network based CAD system. Biomed Signal Process Control 2024;89: 105650. https://doi.org/10.1016/j.bspc.2023.105650.
• [11] S. A. J. J. o. T. AlSHOWARAH and A. I. Technology, “Deep Learning and Statistical Operations Based Features Extraction For Lung Cancer Detection System,” vol. 102, no. 3, 2024. https://www.jatit.org/volumes/Vol102No3/6Vol102No3.pdf.
• [12] D. Drishti, J. J. I. J. o. I. S. Singh, and A. i. Engineering, “Novel Algorithm for Pulmonary Nodule Classification using CNN on CT Scans,” vol. 12, no. 2, pp. 144-152, 2024. https://ijisae.org/index.php/IJISAE/article/view/4237.
• [13] VJ, M.J., S, K. Multi-classification approach for lung nodule detection and classification with proposed texture feature in X-ray images. Multimed Tools Appl 83, 3497-3524 (2024). https://doi.org/10.1007/s11042-023-15281-5.
• [14] Nahiduzzaman M, Goni MOF, Islam MR, Sayeed A, Anower MS, Ahsan M, et al. Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture. Biocybernetics and Biomedical Engineering 2023;43(3):528-50. https://doi.org/10.1016/j.bbe.2023.06.003.
• [15] Bao Y, Li X, Wei W, Qu S, Zhan Y. Simulation on human respiratory motion dynamics and platform construction. Biocybernetics and Biomedical Engineering 2023;43(4):736-50. https://doi.org/10.1016/j.bbe.2023.09.002.
• [16] Kozłowska, E. et al. (2024). Predicting the Risk of Metastatic Dissemination in Nonsmall Cell Lung Cancer Using Clinical and Genetic Data. In: Strumiłło, P., Klepaczko, A., Strzelecki, M., Bociąga, D. (eds) The Latest Developments and Challenges in Biomedical Engineering. PCBEE 2023. Lecture Notes in Networks and Systems, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-031-38430-1_15.
• [17] Yildirim AE, Canayaz M. A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images. Biocybernetics and Biomedical Engineering 2023;43(4):635-55. https://doi.org/10.1016/j.bbe.2023.08.004.
• [18] Liao X, Wu Y, Jiang N, Sun J, Xu W, Gao S, et al. Automated detection of abnormal respiratory sound from electronic stethoscope and mobile phone using MobileNetV2. Biocybernetics and Biomedical Engineering 2023;43(4):763-75. https://doi.org/10.1016/j.bbe.2023.11.001.
• [19] Wu Y, Qi S, Feng J, Chang R, Pang H, Hou J, et al. Attention-guided multiple instance learning for COPD identification: To combine the intensity and morphology. Biocybernetics and Biomedical Engineering 2023;43(3):568-85. https:// doi.org/10.1016/j.bbe.2023.06.004.
• [20] Naqvi NZ, Chhikara M, Garg A, Yashika, Agrawal M. A modified dense-UNet for pulmonary nodule segmentation. Retrieved from INFOCOMP J Comput Sci 2024;22 (2). http://177.105.60.18/index.php/infocomp/article/view/2886.
• [21] Naseer I, Akram S, Masood T, Rashid M, Jaffar A. Lung cancer classification using modified U-Net based lobe segmentation and nodule detection. IEEE Access 2023; 11:60279-91. https://doi.org/10.1109/ACCESS.2023.3285821.
• [22] Selvadass S, Bruntha PM, Sagayam KM, Günerhan H. SAtUNet: Series atrous convolution enhanced U-Net for lung nodule segmentation. Int J Imaging Syst Technol 2024;34(1):e22964. https://doi.org/10.1002/ima.22964.
• [23] Hou J, Yan C, Li R, Huang Q, Fan X, Lin F. Lung nodule segmentation algorithm with SMR-UNet. IEEE Access 2023;11:34319-31. https://doi.org/10.1109/ ACCESS.2023.3264789.
• [24] Ji Z, Zhao Z, Zeng X, Wang J, Zhao L, Zhang X, et al. ResDSda_U-Net: A novel U-Net based residual network for segmentation of pulmonary nodules in lung CT images. IEEE Access 2023. https://doi.org/10.1109/ACCESS.2023.3305270.
• [25] Yang D, Du J, Liu K, Sui Y, Wang J, Gai X. Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation. Technol Health Care 2023;31(S1):477-86. https://doi.org/10.3233/THC-236041.
• [26] Dhivya, P., & Yamini, P. (2024, January). Employing U-NET and RBCNN to Build an Automatic Lung Cancer Segmentation and Classification System. In ICSETPSD 2023: Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India (p. 401). European Alliance for Innovation.
• [27] Takeshita Y, Onozawa S, Katase S, Shirakawa Y, Yamashita K, Shudo J, et al. Evaluation of an artificial intelligence U-net algorithm for pulmonary nodule tracking on chest computed tomography images. J Int Med Res 2024;52(2): 03000605241230033. https://doi.org/10.1177/03000605241230033.
• [28] Shaziya H, Shyamala K. Pulmonary CT images segmentation using CNN and UNet models of deep learning. In: IEEE Pune section international conference (PuneCon). IEEE; 2020. p. 195-201. 10.1109/PuneCon50868.2020.9362463.
• [29] Pourpanah F, Wang R, Lim CP, et al. A review of artificial fish swarm algorithms: recent advances and applications. Artif Intell Rev 2023;56:1867-903. https://doi.org/10.1007/s10462-022-10214-4.
• [30] Alshayeji MH, Abed S. Lung cancer classification and identification framework with automatic nodule segmentation screening using machine learning. Appl Intell 2023;53:19724-41. https://doi.org/10.1007/s10489-023-04552-1.
• [31] Soares AR, Lima TJ, Ricardo de Andrade LR, Rodrigues JJ, Araujo FH. Automatic segmentation of lung nodules in CT images using deep learning. In: 2020 IEEE international conference on e-health networking, application & services (HEALTHCOM). IEEE; 2021. p. 1-6. 10.1109/HEALTHCOM49281.2021.9398925.
• [32] Li R, Xiao C, Huang Y, Hassan H, Huang B. Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: a review. Diagnostics 2022;12(2):298. https://doi.org/10.3390/diagnostics12020298.
• [33] Nguyen TC, Nguyen TP, Cao T, Dao TTP, Ho TN, Nguyen TV, et al. MANet: Multi-branch attention auxiliary learning for lung nodule detection and segmentation. Comput Methods Programs Biomed 2023;241:107748. https://doi.org/10.1016/j. Cmpb.2023.107748.
• [34] Suji RJ, Godfrey WW, Dhar J. Exploring pretrained encoders for lung nodule segmentation task using LIDC-IDRI dataset. Multimed Tools Appl 2024;83: 9685-708. https://doi.org/10.1007/s11042-023-15871-3.
• [35] Khehrah N, Farid MS, Bilal S, Khan MH. Lung nodule detection in CT images using statistical and shape-based features. Journal of Imaging 2020;6(2):6. https://doi. org/10.3390/jimaging6020006.
• [36] Halder A, Dey D. Atrous convolution aided integrated framework for lung nodule segmentation and classification. Biomed Signal Process Control 2023;82:104527. https://doi.org/10.1016/j.bspc.2022.104527.
• [37] Yang Y, Li X, Fu J, Han Z, Gao B. 3D multi-view squeeze-and-excitation convolutional neural network for lung nodule classification. Med Phys 2023;50(3): 1905-16. https://doi.org/10.1002/mp.16221.
• [38] Khan A, Tariq I, Khan H, Khan SU, He N, Zhiyang L, et al. Lung cancer nodules detection via an adaptive boosting algorithm based on self-normalized multiview convolutional neural network. J Oncol 2022;2022(1):5682451. https://doi.org/ 10.1155/2022/5682451.
• [39] Pan Z, Hu G, Zhu Z, Tan W, Han W, Zhou Z, et al. Predicting invasiveness of lung adenocarcinoma at chest CT with deep learning ternary classification models. Radiology 2024;311(1). https://doi.org/10.1148/radiol.232057.
• [40] Atiya SU, Ramesh NVK, Reddy BNK. Classification of non-small cell lung cancers using deep convolutional neural networks. Multimed Tools Appl 2024;83:13261-90. https://doi.org/10.1007/s11042-023-16119-w.
• [41] Swain AK, Swetapadma A, Rout JK, Balabantaray BK. A hybrid learning method for distinguishing lung adenocarcinoma and squamous cell carcinoma. Data Technol Appl 2024;58(1):113-31. https://doi.org/10.1108/DTA-10-2022-0384 Download as.RIS.
• [42] Balcı MA, Batrancea LM, Akgüller Ö, Nichita A. A series-based deep learning approach to lung nodule image classification. Cancers 2023;15(3):843. https://doi. org/10.3390/cancers15030843.
• [43] Nandeesh GS, Nagabushanam M, Pramodkumar S, et al. Lung parenchyma segmentation and nodule detection using deep learning. J Opt 2024;53:635-42. https://doi.org/10.1007/s12596-023-01187-w.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).