Diagnosing and categorizing of pulmonary diseases using Deep learning conventional Neural network

N. Sudhir Reddy; V. Khanaa

doi:10.52756/10.52756/ijerr.2023.v31spl.002

Authors

N. Sudhir Reddy Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu, India https://orcid.org/0009-0004-1132-6863
V. Khanaa Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research (BIHER), Chennai, Tamil Nadu, India https://orcid.org/0000-0002-2509-1549

DOI:

https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.002

Keywords:

Hybrid fuzzy morphological, region of interest, NLTF, laplacian pyramid decomposition

Abstract

Lung cancer is one of the major illnesses that contribute to millions of fatalities worldwide. Numerous deaths could be saved through the early identification and categorization of lung cancers. However, with traditional approaches, classification accuracy cannot be produced. To detect and classify lung diseases, a deep learning convolutional neural network model has been developed. LDDC, the customized local trilateral filter, is used for pre-processing the lung images from computing tomography for non-local trilateral filters. The region of interest for lung cancer was successfully restricted throughout the segmentation of the disease using hybrid fuzzy morphological procedures. To extract the deep seismic features, the Laplacian pyramid decomposition method was utilized for the segmented image. This paper covers an overall analysis of non-local trilateral filter Processing, hybrid fuzzy morphological techniques and analysis of patient and disease characteristics of LIDR- IDRI and FDA data of Group A (no co-AGA), P-value, Multi-mut Patient, Group B (with a co-AGA).

References

Ardila, D. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954-961. https://doi.org/10.1038/s41591-019-0447-x

Asuntha, A., & Andy, S. (2020). Deep learning for lung Cancer detection and classification. Multimedia Tools and Applications, 79(11), 7731-7762. https://doi.org/10.1007/s11042-019-08394-3

Avanzo, M., Stancanello, J., Pirrone, G., & Sartor, G. (2020). Radiomics and deep learning in lung cancer. Strahlentherapie und Onkologie, 196(10), 879-887. https://doi.org/10.1007/s00066-020-01625-9

Bag, S., Golder, R., Sarkar, S., & Maity, S. (2023). SENE: A novel manifold learning approach for distracted driving analysis with spatio-temporal and driver praxeological features. Engineering Applications of Artificial Intelligence, 123, 106332. https://doi.org/10.1016/j.engappai.2023.106332

Bhandary, A. (2020). Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images. Pattern Recognition Letters, 129, 271-278. https://doi.org/10.1016/j.patrec.2019.11.013

Bhatia, S., Yash S., & Lavika G. (2019). Lung cancer detection: a deep learning approach. Soft Computing for Problem Solving. J.C. Bansaletal. (eds.), Soft Computing for Problem Solving, Advancesin Intelligent Systems and Computing 817, Springer, Singapore. pp. 699-705. https://doi.org/10.1007/978-981-13-1595-4_55

Chao, H., Shan, H., Homayounieh, F., Singh, R., Khera, R. D., Guo, H., & Yan, P. (2021). Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography. Nature Communications, 12(1), 1-10. https://doi.org/10.1038/s41467-021-23235-4

Chaunzwa, T. L., Hosny, A., Xu, Y., Shafer, A., Diao, N., Lanuti, M., & Aerts, H. J. (2021). Deep learning classification of lung cancer histology using CT images. Scientific Reports, 11(1), 1-12. https://doi.org/10.1038/s41598-021-84630-x

Das, S., & Sarkar, S. (2022). News media mining to explore speed-crash-traffic association during COVID-19. Transportation Research Record, 03611981221121261. https://doi.org/10.1177/03611981221121261

Dey, P., Chowdhury, S., Abadie, A., Yaroson, E. V., & Sarkar, S. (2023). Artificial Intelligence-Driven Supply Chain Resilience in Vietnamese Manufacturing Small-and Medium-Sized Enterprises. International Journal of Production Research, pp. 1-40. https://doi.org/10.1080/00207543.2023.2179859

Huang, P., Lin, C. T., Li, Y., Tammemagi, M. C., Brock, M. V., Atkar-Khattra, S., & Lam, S. (2019). Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method. The Lancet Digital Health, 1(7), e353-e362. https://doi.org/10.1016/s2589-7500(19)30159-1

Jakimovski, G., & Davcev, D. (2019). Using double convolution neural network for lung cancer stage detection. Applied Sciences: 9(3), 427. https://doi.org/10.3390/APP9030427

Kadir, T., & Gleeson, F. (2018). Lung cancer prediction using machine learning and advanced imaging techniques. Translational Lung Cancer Research: 7(3), 304. https://doi.org/10.21037/tlcr.2018.05.15

Lakshmanaprabu, S. K. (2019). Optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems, 92, 374-382. https://doi.org/10.1016/j.future.2018.10.009

Lee, J. H., Sun, H. Y., Park, S., Kim, H., Hwang, E. J., Goo, J. M., & Park, C. M. (2020). Performance of a deep learning algorithm compared with radiologic interpretation for lung cancer detection on chest radiographs in a health screening population. Radiology, 297(3), 687-696. https://doi.org/10.1148/radiol.2020201240

Masood, A. (2018). Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images. Journal of Biomedical Informatics, 79, 117-128. https://doi.org/10.1016/j.jbi.2018.01.005

Masood, A., Yang, P., Sheng, B., Li, H., Li, P., Qin, J., & Feng, D. D. (2019). Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT. IEEE Journal of Translational Engineering in Health and Medicine, 8, 1-13. https://doi.org/10.1109%2FJTEHM.2019.2955458

Nasrullah, N., Sang, J., Alam, M. S., Mateen, M., Cai, B., & Hu, H. (2019). Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors, 19(17), 3722. https://doi.org/10.3390/s19173722

Ozdemir, O., Rebecca, L. Russell., & Andrew, A. B. (2019). A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low-dose CT scans. IEEE Transactions on Medical Imaging, 39(5), 1419-1429. https://doi.org/10.48550/arXiv.1902.03233

Park, S., Lee, S. M., Do, K. H., Lee, J. G., Bae, W., Park, H., & Seo, J. B. (2019). Deep learning algorithm for reducing CT slice thickness: effect on reproducibility of radiomic features in lung cancer. Korean Journal of Radiology, 20(10), 1431-1440. https://doi.org/10.3348/kjr.2019.0212

Pramanik, A., Sarkar, S., & Maiti, J. (2021). A real-time video surveillance system for traffic pre-events detection. Accident Analysis & Prevention, 154, 106019. https://doi.org/10.1016/j.aap.2021.106019

Pramanik, A. R., Sarkar, S., & Sarkar, B. (2022). OSWMI: An objective-subjective weighted method for minimizing inconsistency in multi-criteria decision making. Computers & Industrial Engineering, 169, 108138. https://doi.org/10.1016/j.cie.2022.108138

Polat, H., & Homay, D. M. (2019). Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture. Applied Sciences, 9(5), 940. https://doi.org/10.3390/app9050940

Punithavathy, K., Sumathi, P., & Ramya, M. M. (2019). Performance evaluation of machine learning techniques in lung cancer classification from PET/CT images. FME Transactions, 47(3), 418-423. https://doi.org/10.5937/fmet1903418p

Qin, R., Wang, Z., Jiang, L., Qiao, K., Hai, J., Chen, J., & Yan, B. (2020). Fine-grained lung cancer classification from PET and CT images based on multidimensional attention mechanism. Complexity, 2020, 6153657 (pp. 1-12). https://doi.org/10.1155/2020/6153657

Riquelme, D., & Moulay, A. (2020). Deep learning for lung cancer nodules detection and classification in CT scans. AI: 1(1), 28-67. https://doi.org/10.3390/ai1010003

Ruan, J. (2022). Development of deep learning-based automatic scan range setting model for lung cancer screening low-dose CT imaging. Academic Radiology, 29(10), 1541-1551. https://doi.org/10.1016/j.acra.2021.12.001

Saha, A., & Yadav, R. (2023). Study on segmentation and prediction of lung cancer based on machine learning approaches. International Journal of Experimental Research and Review, 30, 1-14. https://doi.org/10.52756/ijerr.2023.v30.001

Sarkar, S., Pramanik, A., Maiti, J., & Reniers, G. (2020). Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data. Safety Science, 125, 104616. https://doi.org/10.1016/j.ssci.2020.104616

Sarkar, S., Vinay, S., Djeddi, C., & Maiti, J. (2021). Text mining-based association rule mining for incident analysis: a case study of a steel plant in India. In Proceedings: Pattern Recognition and Artificial Intelligence. 4th Mediterranean Conference, MedPRAI 2020, Hammamet, Tunisia, pp. 257-273. https://doi.org/10.1007/978-3-030-71804-6_19

Sarkar, S., Vinay, S., Raj, R., Maiti, J., & Mitra, P. (2019). Application of optimized machine learning techniques for prediction of occupational accidents. Computers & Operations Research, 106, 210-224. https://doi.org/10.1016/j.cor.2018.02.021

Schwyzer, M. (2018). Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks- initial results. Lung Cancer, 126, 170-173. https://doi.org/10.1016/j.lungcan.2018.11.001

Shakeel, P.M., Burhanuddin, M. A., & Mohammad, I. D. (2022). Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier. Neural Computing and Applications, 34(12), 9579–9592. https://doi.org/10.1007/s00521-020-04842-6

Sharif, M.I. (2020). A comprehensive review on multi-organs tumor detection based on machine learning. Pattern Recognition Letters, 131, 30-37. https://doi.org/10.1016/j.patrec.2019.12.006

Singh, G.A.P., & Gupta, P.K. (2019). Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Computing and Applications, 31(10), 6863- 6877. https://www.springerprofessional.de/en/performance-analysis-of-various-machine-learning-based-approache/15741460

Thakur, S.K., Singh, D.P., & Choudhary, J. (2020). Lung cancer identification: a review on detection and classification. Cancer and Metastasis Reviews, 39(3), 989-998. https://doi.org/10.1007/s10555-020-09901-x

Toğaçar, M., Burhan, E., & Zafer, C. (2020). Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks. Biocybernetics and Biomedical Engineering, 40(1), 23-39. https://doi.org/10.1016/j.bbe.2019.11.004

Wang, S. (2019). Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. European Respiratory Journal, 53(3), 1800986. https://doi.org/10.1183/13993003.00986-2018

Yu, K.H. (2020). Reproducible machine learning methods for lung cancer detection using computed tomography images: Algorithm development and validation. Journal of Medical Internet Research, 22(8), e16709. https://doi.org/10.2196/16709