Hybrid approach for lung cancer detection based on deep learning/machine learning
Abstract
The incidence of Lung Cancer (LC) is rising in India. LC has been diagnosed and detected numerous times utilizing numerous data processing and identification strategies. Since the underlying origin of LC is still unknown, treatment is hopeless, making early diagnosis of lung tumors the only viable treatment option. So, a Machine Learning (ML) and Deep Learning (DL) based system is utilized to categorize CT scans for the existence of LC. The Visual Geometry Group (VGG-16) and Multi-Class Support Vector Machine (VGG-16+MSVM) technique is proposed in this research. Non-Local Means (NLM) Filter and Bi-Histogram Equalization (Bi-HE) are used, respectively; to filter out unwanted background noise in raw data samples and improve image quality. To isolate tumors in the raw data, the K-Means Clustering (KMC) technique is applied. The Gray Level Co-Occurrence Matrix (GLCM) is employed to generate features from the segmented data. The proposed approach is optimized with the use of a Genetic Algorithm (GA) that selects optimal feature subsets to maximize its performance. Combining ML and DL methods in Medical Image Processing is the most effective approach to detecting LC and its stages with the hope of achieving more precise findings. When accuracy is assessed and compared to other procedures, it becomes clear that the suggested methodology is more accurate (95%).
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1. Abdullah DM, Ahmed NS. A review of most recent LC detection techniques using machine learning. International Journal of Science and Business. 2021; 5(3): 159-173.
2. Wang X, Guo Y, Liu L, et al. YAP1 protein expression has variant prognostic significance in small cell lung cancer (SCLC) stratified by histological subtypes. Lung Cancer. 2021; 160: 166-174. doi: 10.1016/j.lungcan.2021.06.026
3. Sardarabadi P, Kojabad AA, Jafari D, et al. Liquid Biopsy-Based Biosensors for MRD Detection and Treatment Monitoring in Non-Small Cell Lung Cancer (NSCLC). Biosensors. 2021; 11(10): 394. doi: 10.3390/bios11100394
4. Saab MM, McCarthy M, O’Driscoll M, et al. A systematic review of interventions to recognise, refer and diagnose patients with lung cancer symptoms. npj Primary Care Respiratory Medicine. 2022; 32(1). doi: 10.1038/s41533-022-00312-9
5. Wang X, Ricciuti B, Nguyen T, et al. Association between Smoking History and Tumor Mutation Burden in Advanced Non–Small Cell Lung Cancer. Cancer Research. 2021; 81(9): 2566-2573. doi: 10.1158/0008-5472.can-20-3991
6. Xu K, Zhang C, Du T, et al. Progress of exosomes in the diagnosis and treatment of lung cancer. Biomedicine & Pharmacotherapy. 2021; 134: 111111. doi: 10.1016/j.biopha.2020.111111
7. Xu Y, Wang Y, Razmjooy N. Lung cancer diagnosis in CT images based on Alexnet optimized by modified Bowerbird optimization algorithm. Biomedical Signal Processing and Control. 2022; 77: 103791. doi: 10.1016/j.bspc.2022.103791
8. Dunke SR, Tarade SS. LC Detection Using Deep Learning. International Journal of Research Publication and Reviews.
9. Shanthi S, Rajkumar N. Lung Cancer Prediction Using Stochastic Diffusion Search (SDS) Based Feature Selection and Machine Learning Methods. Neural Processing Letters. 2020; 53(4): 2617-2630. doi: 10.1007/s11063-020-10192-0
10. Lin CJ, Yang TY. A Fusion-Based Convolutional Fuzzy Neural Network for LC Classification. International Journal of Fuzzy Systems. 2022; 1-17.
11. Alyami J, Khan AR, Bahaj SA, et al. Microscopic handcrafted features selection from computed tomography scans for early stage lungs cancer diagnosis using hybrid classifiers. Microscopy Research and Technique. 2022; 85(6): 2181-2191. doi: 10.1002/jemt.24075
12. Desai U, Kamath S, Shetty AD, Prabhu MS. Computer-Aided Detection for Early Detection of LC Using CT Images. In: Intelligent Sustainable Systems. Springer; 2022.
13. Primakov SP, Ibrahim A, van Timmeren JE, et al. Automated detection and segmentation of non-small cell lung cancer computed tomography images. Nature Communications. 2022; 13(1). doi: 10.1038/s41467-022-30841-3
14. Tumuluru P, Hrushikesava Raju S, Santhi MVBT, et al. Smart LC Detector Using a Novel Hybrid for Early Detection of LC. In: Inventive Communication and Computational Technologies. Springer; 2022.
15. Bai Y, Li D, Duan Q, et al. Analysis of high-resolution reconstruction of medical images based on deep convolutional neural networks in lung cancer diagnostics. Computer Methods and Programs in Biomedicine. 2022; 217: 106592. doi: 10.1016/j.cmpb.2021.106592
16. Selvapandian A, Prabhu SN, Sivakumar P, et al. Lung Cancer Detection and Severity Level Classification Using Sine Cosine Sail Fish Optimization Based Generative Adversarial Network with CT Images. The Computer Journal. 2021; 65(6): 1611-1630. doi: 10.1093/comjnl/bxab141
17. Manoharan H, Rambola RK, Kshirsagar PR, et al. Aerial Separation and Receiver Arrangements on Identifying Lung Syndromes Using the Artificial Neural Network. Computational Intelligence and Neuroscience. 2022; 2022: 1-8. doi: 10.1155/2022/7298903
18. Sutedja G. New techniques for early detection of lung cancer. European Respiratory Journal. 2003; 21(Supplement 39): 57S-66s. doi: 10.1183/09031936.03.00405303
19. Teixeira VH, Pipinikas CP, Pennycuick A, et al. Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions. Nature Medicine. 2019; 25(3): 517-525. doi: 10.1038/s41591-018-0323-0
20. Jain D, Roy-Chowdhuri S. Molecular Pathology of Lung Cancer Cytology Specimens: A Concise Review. Archives of Pathology & Laboratory Medicine. 2018; 142(9): 1127-1133. doi: 10.5858/arpa.2017-0444-ra
21. Dong Z, Li H, Zhou J, et al. The value of cell block based on fine needle aspiration for lung cancer diagnosis. Journal of Thoracic Disease. 2017; 9(8): 2375-2382. doi: 10.21037/jtd.2017.07.91
22. Peng J, Pond G, Donovan E, et al. A Comparison of Radiation Techniques in Patients Treated With Concurrent Chemoradiation for Stage III Non-Small Cell Lung Cancer. International Journal of Radiation Oncology*Biology*Physics. 2020; 106(5): 985-992. doi: 10.1016/j.ijrobp.2019.12.027
23. Lindeman NI, Cagle PT, Aisner DL, et al. Updated Molecular Testing Guideline for the Selection of Lung Cancer Patients for Treatment with Targeted Tyrosine Kinase Inhibitors. Journal of Thoracic Oncology. 2018; 13(3): 323-358. doi: 10.1016/j.jtho.2017.12.001
24. Kadir T, Gleeson F. Lung cancer prediction using machine learning and advanced imaging techniques. Translational Lung Cancer Research. 2018; 7(3): 304-312. doi: 10.21037/tlcr.2018.05.15
25. Schwyzer M, Ferraro DA, Muehlematter UJ, et al. Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – Initial results. Lung Cancer. 2018; 126: 170-173. doi: 10.1016/j.lungcan.2018.11.001
26. Li RY, Liang ZY. Circulating tumor DNA in lung cancer: real-time monitoring of disease evolution and treatment response. Chinese Medical Journal. 2020; 133(20): 2476-2485. doi: 10.1097/cm9.0000000000001097
27. Mazzone PJ, Silvestri GA, Patel S, et al. Screening for Lung Cancer. Chest. 2018; 153(4): 954-985. doi: 10.1016/j.chest.2018.01.016
28. Josipovic M, Aznar MC, Thomsen JB, et al. Deep inspiration breath hold in locally advanced lung cancer radiotherapy: validation of intrafractional geometric uncertainties in the INHALE trial. The British Journal of Radiology. 2019; 92(1104). doi: 10.1259/bjr.20190569
29. Pastorino U, Silva M, Sestini S, et al. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Annals of Oncology. 2019; 30(7): 1162-1169. doi: 10.1093/annonc/mdz117
30. Teo PT, Bajaj A, Randall J, et al. Deterministic small‐scale undulations of image‐based risk predictions from the deep learning of lung tumors in motion. Medical Physics; 2022.
31. Ajitha E, Diwan B, Roshini M. March. LC Prediction using Extended KNN Algorithm. In: 2022 6th International Conference on Computing Methodologies and Communication (ICCMC).
32. Chaunzwa TL, Hosny A, Xu Y, et al. Deep learning classification of lung cancer histology using CT images. Scientific Reports. 2021; 11(1). doi: 10.1038/s41598-021-84630-x
33. Maurer A. An Early Prediction of LC using CT Scan Images. Journal of Computing and Natural Science. 2021; 39-44.
34. Sori WJ, Feng J, Godana AW, et al. DFD-Net: lung cancer detection from denoised CT scan image using deep learning. Frontiers of Computer Science. 2020; 15(2). doi: 10.1007/s11704-020-9050-z
35. Lin CJ, Yang TY. A Fusion-Based Convolutional Fuzzy Neural Network for LC Classification. International Journal of Fuzzy Systems. 2022.
DOI: https://doi.org/10.32629/jai.v7i5.1605
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