An Effective Lung Cancer Diagnosis Model Using the CNN Algorithm

Sonia Kukreja; Munish Sabharwal

doi:10.4108/eetpht.10.6805

Authors

Sonia Kukreja Galgotias University
Munish Sabharwal Galgotias University

DOI:

https://doi.org/10.4108/eetpht.10.6805

Keywords:

Random Forest, Image classification, Deep learning, CT scan, CNN

Abstract

The disease known as lung cancer is a serious condition that may be deadly if it is not diagnosed at an early stage. The diagnosis of lung cancer has to be improved, and there is a need for a cost-effective and user-friendly system that leverages state-of-the-art data science technology. This would help simplify operations, save time and money, and improve diagnosis. This research suggests the use of a convolutional neural network (CNN) architecture for the purpose of categorizing three unique histopathological pictures, namely benign, adenocarcinoma, and squamous cell carcinoma. The purpose of this study is to apply the CNN model to properly classify these three kinds of cancers and to compare the accuracy of the CNN model to the accuracy of other techniques that have been employed in investigations that are comparable to this one. The CNN model was not used in any of the preceding research for the purpose of categorizing these particular histopathological pictures; hence, the relevance of this work cannot be overstated. It is possible to get more positive treatment results by correctly classifying malignant tumors as early as possible. In training, the CNN model obtained an accuracy of 96.11%, and in validation, it earned an accuracy of 97.2%. The suggested method has the potential to improve lung cancer diagnosis in patients by classifying them into subgroups according to the symptoms they exhibit. This approach to machine learning, which makes use of the random forest technique, has the potential to reduce the amount of time, resources, and labor required. Utilizing the CNN model to categorize histopathological pictures may, ultimately, improve the diagnostic accuracy of lung cancer and save lives by allowing early disease identification.

Downloads

Download data is not yet available.

References

[1] Moradi, P., & Jamzad, M. (2019, March). Detecting lung cancer lesions in CT images using 3D convolutional neural networks. In 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA) (pp. 114-118). IEEE.

[2] Jothilakshmi, R., Ramya, M., Prajwala, G., & Ramya Geetha, S. V. (2020). Early lung cancer detection using machine learning and image processing. J Eng Sci, 11(7), 510-514.

[3] Bhandary, A., Prabhu, G. A., Rajinikanth, V., Thanaraj, K. P., Satapathy, S. C., Robbins, D. E., ... & Raja, N. S. M. (2020). Deeplearning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images. Pattern Recognition Letters, 129, 271-278.

[4] Khan, A. (2021). Identification of lung cancer using convolutional neural networks based classification. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 192-203.

[5] Pandian, R., Vedanarayanan, V., Kumar, D. R., & Rajakumar, R. (2022). Detection and classification of lung cancer using CNN and Google net. Measurement: Sensors, 24, 100588.

[6] Kumar, R. R., Polepaka, S., Likithasree, D., & Keerthika, S. (2023, January). An Investigation on CNN-based Lung Cancer Prediction Method. In 2023 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.

[7] Abdullah, D. M., Abdulazeez, A. M., & Sallow, A. B. (2021). Lung cancer prediction and classification based on correlation selection method using machine learning techniques. Qubahan Academic Journal, 1(2), 141-149.

[8] Anil Kumar, C., Harish, S., Ravi, P., Svn, M., Kumar, B. P., Mohanavel, V., ... & Asfaw, A. K. (2022). Lung cancer prediction from text datasets using machine learning. BioMed Research International, 2022.

[9] Sasikala, S., Bharathi, M., & Sowmiya, B. R. (2018). Lung cancer detection and classification using deep CNN. international journal of innovative technology and exploring engineering, 8(25), 259-262.

[10] Hatuwal, B. K., & Thapa, H. C. (2020). Lung cancer detection using convolutional neural network on histopathological images. Int. J. Comput. Trends Technol, 68(10), 21-24.

[11] Jain, D., Singh, P., Pandey, A. K., Singh, M., Singh, H., & Singh, A. (2022, November). Lung Cancer Detection Using Convolutional Neural Network. In 2022 3rd International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (pp. 1-4). IEEE.

[12] Shah, A. A., Malik, H. A. M., Muhammad, A., Alourani, A., & Butt, Z. A. (2023). Deep learning ensemble 2D CNN approach towards the detection of lung cancer. Scientific Reports, 13(1), 2987.

[13] Onozato, Y., Iwata, T., Uematsu, Y., Shimizu, D., Yamamoto, T., Matsui, Y., ... & Yoshino, I. (2023). Predicting pathological highly invasive lung cancer from preoperative [18F] FDG PET/CT with multiple machine learning models. European Journal of Nuclear Medicine and Molecular Imaging, 50(3), 715-726.

[14] Huang, T., Le, D., Yuan, L., Xu, S., & Peng, X. (2023). Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit. Plos one, 18(1), e0280606.

[15] Chandran, U., Reps, J., Yang, R., Vachani, A., Maldonado, F., & Kalsekar, I. (2023). Machine learning and real-world data to predict lung cancer risk in routine care. Cancer Epidemiology, Biomarkers & Prevention, 32(3), 337-343.

[16] Adams, S. J., Mikhael, P., Wohlwend, J., Barzilay, R., Sequist, L. V., & Fintelmann, F. J. (2023). Artificial intelligence and machine learning in lung cancer screening. Thoracic Surgery Clinics, 33(4), 401-409.

[17] Khouadja, O., & Naceur, M. S. (2023, April). Lung Cancer Detection with Machine Learning and Deep Learning: A Narrative Review. In 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET) (pp. 1-8). IEEE.

[18] Kong, C., Lai, L., Jin, X., Chen, W., Ding, J., Zheng, L., ... & Ji, J. (2023). Machine Learning Classifier for Preoperative Prediction of Early Recurrence After Bronchial Arterial Chemoembolization Treatment in Lung Cancer Patients. Academic Radiology.

[19] Ojha, T. R. (2023). Machine Learning based Classification and Detection of Lung Cancer. Journal of Artificial Intelligence, 5(2), 110-128.

[20] Pogue, J. A., Cardenas, C. E., Harms, J., Soike, M. H., Kole, A. J., Schneider, C. S., ... & Stanley, D. N. (2023). Benchmarking automated machine learning enhanced planning with Ethos against manual and knowledge-based planning for locally advanced lung cancer. Advances in Radiation Oncology, 101292.

[21] Chassagnon, G., De Margerie-Mellon, C., Vakalopoulou, M., Marini, R., Hoang-Thi, T. N., Revel, M. P., & Soyer, P. (2023). Artificial intelligence in lung cancer: current applications and perspectives. Japanese Journal of Radiology, 41(3), 235-244.

[22] Huang, S., Yang, J., Shen, N., Xu, Q., & Zhao, Q. (2023, January). Artificial intelligence in lung cancer diagnosis and prognosis: current application and future perspective. In Seminars in Cancer Biology. Academic Press.

[23] Saravanan, T. M., Jagadeesan, M., PA, S., Mubarak, M. M., Kumar, S. P., & Sanjay, S. (2023, January). A CNN Based Machine Learning Scheme to Detect Lung Cancer Detection from CT Scan Images. In 2023 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.

[24] Bhattacharjee, S., Saha, B., & Saha, S. (2023, January). Prediction of Recurrence in Non Small Cell Lung Cancer Patients with Gene Expression Data Using Machine Learning Techniques. In 2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE) (pp. 1-8). IEEE.

[25] Fatoki, F. M., Akinyemi, E. K., & Phlips, S. A. (2023). Prediction of Lungs Cancer Diseases Datasets Using Machine Learning Algorithms. Current Journal of Applied Science and Technology, 42(11), 15-23

[26] Mohammed, B. A., Senan, E. M., Alshammari, T. S., Alreshidi, A., Alayba, A. M., Alazmi, M., & Alsagri, A. N. (2023). Hybrid Techniques of Analyzing MRI Images for Early Diagnosis of Brain Tumours Based on Hybrid Features. Processes, 11(1), 212. MDPI AG. Retrieved from http://dx.doi.org/10.3390/pr11010212

[27] Minna, J. D., Roth, J. A., & Gazdar, A. F. (2002). Focus on lung cancer. Cancer cell, 1(1), 49-52.

[28] Leiter, A., Veluswamy, R. R., & Wisnivesky, J. P. (2023). The global burden of lung cancer: current status and future trends. Nature Reviews Clinical Oncology, 20(9), 624-639.

[29] Oliver, A. L. (2022). Lung cancer: Epidemiology and screening. Surgical Clinics, 102(3), 335-344.

[30] Al Bakir, M., Huebner, A., Martínez-Ruiz, C., Grigoriadis, K., Watkins, T. B., Pich, O., ... & Swanton, C. (2023). The evolution of non-small cell lung cancer metastases in TRACERx. Nature, 1-10.

[31] Azween Abdullah, S. Nithya, M. Mary Shanthi Rani, S. Vijayalakshmi and Balamurugan Balusamy, “Stacked LSTM and Kernel-PCA-based Ensemble Learning for Cardiac Arrhythmia Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 14(9), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140905

[32] Tanwar, Sushama & Vijayalakshmi, S. & Sabharwal, Munish & Kaur, Manjit & Ali, Ahmad & Lee, Heung-No. (2022). Detection and Classification of Colorectal Polyp Using Deep Learning. BioMed Research International. 2022. 1-9. 10.1155/2022/2805607.

[33] Gokul Rajan V., S. Vijayalakshmi, Munish Sabharwal. (2021). Modified CNN based Feature Extraction for Sclera Recognition of Off . Annals of the Romanian Society for Cell Biology, 2579–2590.

[34] P. Durgadevi, S.Vijayalakshmi, Munish Sabharwal. (2021). Fetal Brain Abnormality Detection through PSO (Particle Swarm Optimization) and Volume Estimation. Annals of the Romanian Society for Cell Biology, 2700–2714.