Detection of Lung Cancer Cells Using Deep Learning Methods

Year 2024, , 445 - 459, 29.06.2024

Muhittin Genç , Funda Akar

https://doi.org/10.17798/bitlisfen.1422869

Abstract

Lung cancer stands out as a high mortality, fatal disease worldwide. Early diagnosis is crucial for effective treatment of this disease; however, treatment options can be limited when it is often diagnosed in advanced stages. This study examines the role of artificial intelligence (AI) techniques in early diagnosis of lung cancer and emphasizes the advantages it provides. Particularly, the ability of deep learning algorithms to extract meaningful features from complex datasets indicates significant potential for detecting early stages of lung cancer. In this context, it is anticipated that AI-supported diagnostic systems have the potential to significantly improve lung cancer diagnostic methods by reducing the workload of radiologists and increasing accuracy rates. In this study, a total of 6 datasets were obtained by applying Gabor filter and Histogram Equalization+CLAHE filter to original datasets. The results obtained in the diagnosis of lung cancer using Convolutional Neural Networks (CNN) and YOLO algorithms are evaluated in two different categories. One of these categories is the investigation of the effect of image preprocessing methods. The other is the investigation of the effect of dataset partitioning into training, testing, and validation on success. According to the results obtained, the highest success rate in terms of F1 Score for the CNN model was achieved in both dataset partitioning (70%-20%-10% and 60%-20%-20%) with the datasets subjected to Histogram Equalization+CLAHE filter. It was obtained as 99%. For the YOLO model, the highest success rate was determined as 96% F1 Score with the same preprocessing technique and dataset partition. The effect of image preprocessing and dataset partitioning on success is not as high in the YOLO model as it is in the CNN model.

Keywords

CNN, Deep Learning, Lung Cancer Detection, YOLO.

References

• [1] C. Yan and N. Razmjooy, “Optimal lung cancer detection based on CNN optimized and improved Snake optimization algorithm,” Biomed Signal Process Control, vol. 86, p. 105319, 2023, doi: https://doi.org/10.1016/j.bspc.2023.105319.
• [2] Global Cancer Observatory, “World,” 2020. Accessed: Dec. 02, 2023. [Online]. Available: https://gco.iarc.fr/today/data/factsheets/populations/900-world-fact-sheets.pdf
• [3] Global Cancer Observatory, “Turkey,” 2020. Accessed: Jan. 18, 2024. [Online]. Available: https://gco.iarc.fr/today/data/factsheets/populations/792-turkey-fact-sheets.pdf
• [4] M. Keshani, Z. Azimifar, F. Tajeripour, and R. Boostani, “Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system,” Comput Biol Med, vol. 43, no. 4, pp. 287–300, May 2013, doi: 10.1016/j.compbiomed.2012.12.004.
• [5] J. Kuruvilla and K. Gunavathi, “Lung cancer classification using neural networks for CT images,” Comput Methods Programs Biomed, vol. 113, no. 1, pp. 202–209, Jan. 2014, doi: 10.1016/j.cmpb.2013.10.011.
• [6] A. O. De Carvalho Filho, W. B. De Sampaio, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, “Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index,” Artif Intell Med, vol. 60, no. 3, pp. 165–177, 2014, doi: 10.1016/j.artmed.2013.11.002.
• [7] Q. Z. Song, L. Zhao, X. K. Luo, and X. C. Dou, “Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images,” J Healthc Eng, vol. 2017, 2017, doi: 10.1155/2017/8314740.
• [8] T. Lustberg et al., “Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer,” Radiotherapy and Oncology, vol. 126, no. 2, pp. 312–317, Feb. 2018, doi: 10.1016/j.radonc.2017.11.012.
• [9] S. K. Lakshmanaprabu, S. N. Mohanty, K. Shankar, N. Arunkumar, and G. Ramirez, “Optimal deep learning model for classification of lung cancer on CT images,” Future Generation Computer Systems, vol. 92, pp. 374–382, Mar. 2019, doi: 10.1016/j.future.2018.10.009.
• [10] P. M. Shakeel, M. A. Burhanuddin, and M. I. Desa, “Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks,” Measurement (Lond), vol. 145, pp. 702–712, Oct. 2019, doi: 10.1016/j.measurement.2019.05.027.
• [11] N. Gupta, D. Gupta, A. Khanna, P. P. Rebouças Filho, and V. H. C. de Albuquerque, “Evolutionary algorithms for automatic lung disease detection,” Measurement, vol. 140, pp. 590–608, 2019, doi: https://doi.org/10.1016/j.measurement.2019.02.042.
• [12] G. Kasinathan, S. Jayakumar, A. H. Gandomi, M. Ramachandran, S. J. Fong, and R. Patan, “Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier,” Expert Syst Appl, vol. 134, pp. 112–119, Nov. 2019, doi: 10.1016/j.eswa.2019.05.041.
• [13] H. F. Al-Yasriy, M. S. Al-Husieny, F. Y. Mohsen, E. A. Khalil, and Z. S. Hassan, “Diagnosis of Lung Cancer Based on CT Scans Using CNN,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing Ltd, Nov. 2020. doi: 10.1088/1757-899X/928/2/022035.
• [14] P. Nanglia, S. Kumar, A. N. Mahajan, P. Singh, and D. Rathee, “A hybrid algorithm for lung cancer classification using SVM and Neural Networks,” ICT Express, vol. 7, no. 3, pp. 335–341, 2021, doi: https://doi.org/10.1016/j.icte.2020.06.007.
• [15] S. Doppalapudi, R. G. Qiu, and Y. Badr, “Lung cancer survival period prediction and understanding: Deep learning approaches,” Int J Med Inform, vol. 148, p. 104371, 2021, doi: https://doi.org/10.1016/j.ijmedinf.2020.104371.
• [16] X. Chen, Q. Duan, R. Wu, and Z. Yang, “Segmentation of lung computed tomography images based on SegNet in the diagnosis of lung cancer,” J Radiat Res Appl Sci, vol. 14, no. 1, pp. 396–403, Dec. 2021, doi: 10.1080/16878507.2021.1981753.
• [17] N. Baranwal, P. Doravari, and R. Kachhoria, “Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN).” [Online]. Available: https://orcid.org/0000-0002-1113-7884
• [18] Md. A. Talukder, Md. M. Islam, M. A. Uddin, A. Akhter, K. F. Hasan, and M. A. Moni, “Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning,” Expert Syst Appl, vol. 205, p. 117695, 2022, doi: https://doi.org/10.1016/j.eswa.2022.117695.
• [19] A. B. Pawar et al., “Implementation of blockchain technology using extended CNN for lung cancer prediction,” Measurement: Sensors, vol. 24, p. 100530, 2022, doi: https://doi.org/10.1016/j.measen.2022.100530.
• [20] B. Haznedar and N. Y. Simsek, “A Comparative Study on Classification Methods for Renal Cell and Lung Cancers Using RNA-Seq Data,” IEEE Access, vol. 10, pp. 105412–105420, 2022, doi: 10.1109/ACCESS.2022.3211505.
• [21] C. Loraksa, S. Mongkolsomlit, N. Nimsuk, M. Uscharapong, and P. Kiatisevi, “Development of the Osteosarcoma Lung Nodules Detection Model Based on SSD-VGG16 and Competency Comparing With Traditional Method,” IEEE Access, vol. 10, pp. 65496–65506, 2022, doi: 10.1109/ACCESS.2022.3183604.
• [22] S. Mehmood et al., “Malignancy Detection in Lung and Colon Histopathology Images Using Transfer Learning With Class Selective Image Processing,” IEEE Access, vol. 10, pp. 25657–25668, 2022, doi: 10.1109/ACCESS.2022.3150924.
• [23] M. Kanipriya, C. Hemalatha, N. Sridevi, S. R. SriVidhya, and S. L. Jany Shabu, “An improved capuchin search algorithm optimized hybrid CNN-LSTM architecture for malignant lung nodule detection,” Biomed Signal Process Control, vol. 78, p. 103973, 2022, doi: https://doi.org/10.1016/j.bspc.2022.103973.
• [24] S. Shanthi, V. S. Akshaya, J. A. Smitha, and M. Bommy, “Hybrid TABU search with SDS based feature selection for lung cancer prediction,” International Journal of Intelligent Networks, vol. 3, pp. 143–149, 2022, doi: https://doi.org/10.1016/j.ijin.2022.09.002.
• [25] A. A. Shah, H. A. M. Malik, A. Muhammad, A. Alourani, and Z. A. Butt, “Deep learning ensemble 2D CNN approach towards the detection of lung cancer,” Sci Rep, vol. 13, no. 1, p. 2987, 2023, doi: 10.1038/s41598-023-29656-z.
• [26] K. Clark et al., “The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository,” Journal of Digital Imaging.
• [27] Mehmet Fatih AKCA, “Veri seti.” Accessed: Jan. 19, 2024. [Online]. Available: https://universe.roboflow.com/mehmet-fatih-akca/yolotransfer/dataset/2
• [28] R. Chauhan, K. K. Ghanshala, and R. C. Joshi, “Convolutional Neural Network (CNN) for Image Detection and Recognition,” in 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018, pp. 278–282. doi: 10.1109/ICSCCC.2018.8703316.