Conference Paper2023Cited by 1

A Framework for Lung Cancer Detection at Early Stages with IoT and Decision Support System

A. I. Chowdhury, S. Jahan, T. M. Mahee, M. I. Khan, M. T. Habib, A. Islam

2023 IEEE International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

IEEE, pp. 262-268

  • Good Health and Well-being — Relevance: 53%
DOI: 10.1109/3ict60104.2023.10391757URL

Abstract

Cancer continues to be a pervasive and alarming threat in today’s society, with lung cancer ranking among the most perilous forms of this disease. Fortunately, the rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized the healthcare industry, offering promising avenues for lung cancer detection. In this paper, a comparative analysis of existing literature is conducted to provide a deeper understanding of the different approaches utilized in the lung cancer detection process. Drawing upon these findings, an innovative framework based on a decision support system (DSS) and IoT is proposed to facilitate faster and earlier detection of lung cancer to assist physicians in making informed treatment recommendations following the detection. By integrating IoT with a DSS, this paper advocates for improved early detection of lung cancer, leveraging insights from existing literature on machine learning (ML), deep learning (DL), and IoT-based diagnosis of lung cancer.

Keywords

  • Deep learning
  • Internet of Things
  • lung cancer detection
  • Decision support system
  • early cancer detection
  • treatment guidelines
  • Machine learning

References

  1. 1.Kee Yuan Ngiam, Ing Wei Khor. (2019). Big data and machine learning algorithms for health-care delivery. The Lancet Oncology, 20(5), e262–e273[10.1016/s1470-2045(19)30149-4]
  2. 2.Lakshmanaprabu S.K., Sachi Nandan Mohanty, Shankar K., Arunkumar N., Gustavo Ramirez. (2018). Optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems, 92, 374–382[10.1016/j.future.2018.10.009]
  3. 3.Abhir Bhandary, G. Ananth Prabhu, V. Rajinikanth, K. Palani Thanaraj, Suresh Chandra Satapathy, David E. Robbins, Charles Shasky, Yu-Dong Zhang, João Manuel R.S. Tavares, N. Sri Madhava Raja. (2019). Deep-learning framework to detect lung abnormality – A study with chest X-Ray and lung CT scan images. Pattern Recognition Letters, 129, 271–278[10.1016/j.patrec.2019.11.013]
  4. 4.A. Asuntha, Andy Srinivasan. (2020). Deep learning for lung Cancer detection and classification. Multimedia Tools and Applications, 79(11-12), 7731–7762[10.1007/s11042-019-08394-3]
  5. 5.Wafaa Alakwaa, Mohammad Nassef, Amr Badr. (2017). Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN). International Journal of Advanced Computer Science and Applications, 8(8)[10.14569/ijacsa.2017.080853]
  6. 6.Anum Masood, Bin Sheng, Ping Li, Xuhong Hou, Xiaoer Wei, Jing Qin, Dagan Feng. (2018). Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images. Journal of Biomedical Informatics, 79, 117–128[10.1016/j.jbi.2018.01.005]
  7. 7.Hemantha Krishna Bharadwaj, Aayush Agarwal, Vinay Chamola, Naga Rajiv Lakkaniga, Vikas Hassija, Mohsen Guizani, Biplab Sikdar. (2021). A Review on the Role of Machine Learning in Enabling IoT Based Healthcare Applications. IEEE Access, 9, 38859–38890[10.1109/access.2021.3059858]
  8. 8.Siddharth Bhatia, Yash Sinha, Lavika Goel. (2018). Lung Cancer Detection: A Deep Learning Approach. Advances in intelligent systems and computing, 699–705[10.1007/978-981-13-1595-4_55]
  9. 9.Nuruzzaman Faruqui, Mohammad Abu Yousuf, Md Whaiduzzaman, A.K.M. Azad, Alistair Barros, Mohammad Ali Moni. (2021). LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data. Computers in Biology and Medicine, 139, 104961[10.1016/j.compbiomed.2021.104961]
  10. 10.Karla Sánchez-Lara, Jenny G Turcott, Eva Juárez, Patricia Guevara, Carolina Núñez-Valencia, Luis F. Oñate-Ocaña, Diana Flores, Oscar Arrieta. (2012). Association of Nutrition Parameters Including Bioelectrical Impedance and Systemic Inflammatory Response With Quality of Life and Prognosis in Patients With Advanced Non-Small-Cell Lung Cancer: A Prospective Study. Nutrition and Cancer, 64(4), 526–534[10.1080/01635581.2012.668744]
  11. 11.Elias Dritsas, Maria Trigka. (2022). Lung Cancer Risk Prediction with Machine Learning Models. Big Data and Cognitive Computing, 6(4), 139[10.3390/bdcc6040139]
  12. 12.Wasudeo Rahane, Himali Dalvi, Yamini Magar, Anjali Kalane, Satyajeet Jondhale. (2018). Lung Cancer Detection Using Image Processing and Machine Learning HealthCare. 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), 1–5[10.1109/icctct.2018.8551008]
  13. 13.Janee Alam, Sabrina Alam, Alamgir Hossan. (2018). Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifie. 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), 1–4[10.1109/ic4me2.2018.8465593]
  14. 14.José Marcio Luna, Hann-Hsiang Chao, Eric S. Diffenderfer, Gilmer Valdes, Chidambaram Chinniah, Grace Ma, Keith A. Cengel, Timothy D. Solberg, Abigail T. Berman, Charles B. Simone. (2019). Predicting radiation pneumonitis in locally advanced stage II–III non-small cell lung cancer using machine learning. Radiotherapy and Oncology, 133, 106–112[10.1016/j.radonc.2019.01.003]
  15. 15.Chi-Hsiang Huang, Chian Zeng, Yi-Chia Wang, Hsin-Yi Peng, Chia-Sheng Lin, Che-Jui Chang, Hsiao-Yu Yang. (2018). A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer. Sensors, 18(9), 2845[10.3390/s18092845]
  16. 16.Alberto Ruano-Raviña, Mariano Provencio, Virginia Calvo de Juan, Enric Carcereny, Teresa Moran, Delvys Rodriguez-Abreu, Rafael López-Castro, Eugenio Cuadrado Albite, María Guirado, Lucía Gómez González, Bartomeu Massutí, Ana Laura Ortega Granados, Ana Blasco, Manuel Cobo, Rosario Garcia-Campelo, Joaquim Bosch, José Trigo, Óscar Juan, Carlos Aguado de la Rosa, Manuel Dómine, María Sala, Juana Oramas, Joaquín Casal-Rubio, Sara Cerezo. (2020). Lung cancer symptoms at diagnosis: results of a nationwide registry study. ESMO Open, 5(6), e001021[10.1136/esmoopen-2020-001021]
  17. 17.D. Palani, K. Venkatalakshmi. (2018). An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification. Journal of Medical Systems, 43(2), 21[10.1007/s10916-018-1139-7]
  18. 18.Nikita Banerjee, Subhalaxmi Das. (2020). Prediction Lung Cancer– In Machine Learning Perspective. 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), 1–5[10.1109/iccsea49143.2020.9132913]
  19. 19.Hamidreza Namazi, Mona Kiminezhadmalaie. (2015). Diagnosis of Lung Cancer by Fractal Analysis of Damaged DNA. Computational and Mathematical Methods in Medicine, 2015, 1–11[10.1155/2015/242695]
  20. 20.Tien Hoang, Suzanne E. Dahlberg, Alan B. Sandler, Julie R. Brahmer, Joan H. Schiller, David H. Johnson. (2012). Prognostic Models to Predict Survival in Non–Small-Cell Lung Cancer Patients Treated with First-Line Paclitaxel and Carboplatin with or without Bevacizumab. Journal of Thoracic Oncology, 7(9), 1361–1368[10.1097/jto.0b013e318260e106]
  21. 21.Dinesh Valluru, I. Jasmine Selvakumari Jeya. (2019). IoT with cloud based lung cancer diagnosis model using optimal support vector machine. Health Care Management Science, 23(4), 670–679[10.1007/s10729-019-09489-x]
  22. 22.Songjing Chen, Sizhu Wu. (2020). Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data. Journal of Medical Internet Research, 22(3), e17695[10.2196/17695]
  23. 23.Ummadi Janardhan Reddy, Busi Reddy Venkata Ramana Reddy, Bodi Eswara Reddy. (2019). Recognition of Lung Cancer Using Machine Learning Mechanisms with Fuzzy Neural Networks. Traitement du signal, 36(1), 87–91[10.18280/ts.360111]
  24. 24.Muthuperumal Periyaperumal Ramkumar, Pauliah David Mano Paul, Balajee Maram, John Patrick Ananth. (2022). Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things. Concurrency and Computation Practice and Experience, 34(25)[10.1002/cpe.7264]
  25. 25.K. Karthick, S. Rajkumar, N. Selvanathan, U.K.Balaji Saravanan, M. Murali, B. Dhiyanesh. (2021). Retracted: Analysis of Lung Cancer Detection Based on the Machine Learning Algorithm and IOT. , 1–8[10.1109/icces51350.2021.9489084]
  26. 26.D. Révész, E. G. Engelhardt, J. J. Tamminga, F. M. N. H. Schramel, B. D. Onwuteaka-Philipsen, E. M. W. van de Garde, E. W. Steyerberg, E. P. Jansma, H. C. W. De Vet, V. M. H. Coupé. (2017). Decision support systems for incurable non-small cell lung cancer: a systematic review. BMC Medical Informatics and Decision Making, 17(1), 144[10.1186/s12911-017-0542-1]
  27. 27.Arvind Kumar Tiwari. (2016). Prediction of Lung Cancer Using Image Processing Techniques: A Review. Advanced Computational Intelligence An International Journal (ACII), 3(1), 1–9[10.5121/acii.2016.3101]
  28. 28.Mustakim Al Helal, Atiqul Islam Chowdhury, Ashraful Islam, Eshtiak Ahmed, Md. Swakshar Mahmud, Sabrina Hossain. (2019). An Optimization Approach to Improve Classification Performance in Cancer and Diabetes Prediction. [10.1109/ecace.2019.8679413]
  29. 29.(2019). Lung Cancer Prediction using Data Mining Techniques. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 12301–12305[10.35940/ijrte.d9914.118419]
  30. 30.Emil Selvan Gnanasigamani Samuel Raj, Issac Diana Jeba Jingle, Balajee Maram, John Patrick Ananth. (2022). <scp>IoT</scp> enabled lung cancer detection and routing algorithm using <scp>CBSOA</scp>‐based <scp>ShCNN</scp>. International Journal of Adaptive Control and Signal Processing, 37(1), 224–243[10.1002/acs.3518]
  31. 31.Amanina Iymia Jeffree, Salmah Karman, Suriani Ibrahim, Mohd Sayuti Ab Karim, Shaifulazuar Rozali. (2019). Biosensors Approach for Lung Cancer Diagnosis—A Review. Lecture notes in mechanical engineering, 425–435[10.1007/978-981-13-8323-6_36]
  32. 32.Kamila Wojas-Krawczyk, Paweł Krawczyk, Radosław Mlak, Tomasz Kucharczyk, Dariusz M. Kowalski, Maciej Krzakowski, Janusz Milanowski. (2011). The Applicability of a Predictive Index for Second- and Third-Line Treatment of Unselected Non-Small-Cell Lung Cancer Patients. Respiration, 82(4), 341–350[10.1159/000322843]
  33. 33.K. Ravindranath, K. Somashekar. (2017). Early detection of lung cancer by nodule extraction — A survey. 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 4, 1–4[10.1109/iceeccot.2017.8284635]
  34. 34.Tim Luckett, Jane Phillips, David C. Currow, Meera Agar, Alex Molassiotis. (2019). Cough in lung cancer: A survey of current practice among Australian health professionals. Collegian Journal of the Royal College of Nursing Australia, 26(6), 629–633[10.1016/j.colegn.2019.09.002]

Cited By

  1. 1.J. S. Sowmiya, R. Ablin. (2026). A Literature Review on Lung Cancer Detection and Classification with Optimal Feature Selection and ML/DL. , 1–7[10.1109/icsft67370.2026.11566351]
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