An Enhanced Lung Cancer Identification and Classification Based on Advanced Deep Learning and Convolutional Neural Network

Authors

  • Ammar Hassan 4dots Solutions, 534 Block G1, Johar Town Lahore, 54000, Pakistan Author
  • Hamayun Khan Department of Computer Science, Faculty of Computer Science & IT Superior University Lahore, 54000, Pakistan Author
  • Arshad Ali Faculty of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, 42351, Saudi Arabia Author
  • Irfan Ud din Department of Computer Science, Faculty of Computer Science & IT Superior University Lahore, 54000, Pakistan Author
  • Abdullah Sajid 4dots Solutions, 534 Block G1, Johar Town Lahore, 54000, Pakistan Author
  • Mohammad Husain Faculty of Computer and Information Systems, Islamic University of Madinah, Al Madinah Al Munawarah, 42351, Saudi Arabia Author
  • Muddassar Ali Department of Computer Science & Information Technology Superior University Lahore, 54000, Pakistan Author
  • Amna Naz Department of Computer Science & Information Technology Superior University Lahore, 54000, Pakistan Author
  • Hanfia Fakhar Department of Computer Science & Information Technology Superior University Lahore, 54000, Pakistan Author

DOI:

https://doi.org/10.61506/01.00308

Keywords:

Computed tomography, Training, Cancer, Cancer detection, Sensitivity, Data Augmentation, Convolutional Neural Network Structure, Convolutional Neural Network

Abstract

In this research, a fast, accurate, and stable system of lung cancer detection based on novel deep learning techniques is proposed. Lung cancer continues to be one of the most monumental global health concerns, which is why there is an urgent need for low-cost and non-invasive screening. Though the diagnostic methods that are most commonly in use include CTscan, X-ray etc. The interpretation by the human eye varies and errors are bound to occur. In response to this challenge, we outline a more automated approach that is based on deep learning models and can be used to classify lung pictures with high levels of accuracy. This research makes use of a large data set of lung scans categorised as normal, malignant, and benign. The first look what the data had in store threw up some correlation with picture size and what seemed to be category differences. Realizing that live feed requires constant input, each picture underwent grayscale conversion and dimensionality reduction. In order to effectively deal with the unbalanced nature of the dataset that was discovered in the study, the Synthetic Minority Oversampling Technique (SMOTE) was applied as a technique. In this presentation, three new designs were introduced: Model I, Model 2, and Model 3. Additionally, one architecture was developed with the purpose of merging the predictions of all three models. Furthermore, out of all the models created, the best model emerged as model 1 with approximately an accuracy of 84%. 7%. But the ensemble strategy which was intended to make the best of each of the models, produced an astounding 82. 5% accuracy. The specific advantages and misclassification behaviors of Model 2 and 3, although less accurate than Model 1 but are currently under evaluation for future Model ensemble improvements. The technique developed using deep learning addresses the challenges at a faster, efficient, and contactless approach to lung cancer analysis. The fact that it is capable of operating in tandem with others diagnostic instruments may help reduce diagnostic errors and enhance patient care. We have addressed this issue so that the various practitioners would be able to read this paper and we can go to the next generation of diagnostic technologies.

References

Adiba, M., Das, T., Paul, A., Das, A., Chakraborty, S., Rosen, M. I., & Nabi, A.H. M. N. (2021). Computational analysis of coding and non-coding SNPs in the androgen receptor gene. Informatics in Medicine Unlocked, 24.

Ahmed, S. F., Alam, M. S. Bin, Afrin, S., Rafa, S. J., Rafa, N., & Gandomi, A.H. (2023). Comprehensive overview of the Internet of Medical Things (IoMT): Data integration, security challenges, and possible solutions. Information Fusion, 102060.

Akhtar, A., Ch, S. N., & Hussain, M. (2021). Computational identification of functional SNPs in CYP2U1 protein associated with hereditary spastic paraplegia. Informatics in Medicine Unlocked, 24.

Akram Ghumman, S., Mahmood, A., Noreen, S., Aslam, A., Ijaz, B., Amanat, A., Kausar, R., Rana, M., & Hameed, H. (2023). Evaluation of chitosan-linseed mucilage polyelectrolyte complex nanoparticles containing Methotrexate for in vitro cytotoxicity and toxicological assessments. Arabian Journal of Chemistry, 16(2).

Alballa, N., & Al-Turaiki, I. (2021). Review of machine learning techniques for diagnosing COVID-19, predicting mortality, and assessing severity risk. Informatics in Medicine Unlocked, 24. Elsevier Ltd.

Al Bassam, N., Hussain, S. A., Al Qaraghuli, A., Khan, J., Sumesh, E. P., & Lavanya, V. (2021). IoT-based wearable technology for monitoring the health of quarantined COVID-19 patients remotely. Informatics in Medicine Unlocked, 24.

Alfarghaly, O., Khaled, R., Elkorany, A., Reial, M., & Fahmy, A. (2021). Utilization of conditioned transformers for automated generation of radiology reports. Informatics in Medicine Unlocked, 24.

Alharthi, A. M., Lee, M. H., & Algamal, Z. Y. (2021). Gene selection and classification of microarray gene expression data using a novel adaptive L1-norm elastic net penalty. Informatics in Medicine Unlocked, 24.

Almustafa, K. M. (2021). Employing various classification algorithms to predict chronic kidney disease. Informatics in Medicine Unlocked, 24.

Alzain, Z., Alfayez, A., Alsalman, D., Alanezi, F., Hariri, B., Al-Rayes, S., Alhodaib, H., & Alanzi, T. (2021). The influence of social media on the training and ongoing education of healthcare professionals in Eastern Saudi Arabia. Informatics in Medicine Unlocked, 24.

Amir-Behghadami, M., & Janati, A. (2021). State Welfare Organization's tele-mental health support during the COVID-19 pandemic in Iran. Informatics in Medicine Unlocked, 24. Elsevier Ltd.

Asadzadeh, A., Samad-Soltani, T., & Rezaei-Hachesu, P. (2021). Utilization of virtual and augmented reality in epidemics of infectious diseases, focusing on COVID-19. Informatics in Medicine Unlocked, 24. Elsevier Ltd.

Br├╝ggemann, S., Chan, T., Wardi, G., Mandel, J., Fontanesi, J., & Bitmead, R. R. (2021). A decision support tool for hospital resource allocation during the COVID-19 crisis. Informatics in Medicine Unlocked, 24.

Bukhari, A., Ijaz, I., Zain, H., Mehmood, U., Mudassir Iqbal, M., Gilani, E., & Nazir, A. (2023). Improving Cr adsorption from petroleum refinery wastewater with CdO nanoparticles incorporated into graphene and graphene oxide nanosheets. Arabian Journal of Chemistry, 16(2).

Chakravadhanula, K. (2021). Smartphone-based test and predictive models for rapid, non-invasive monitoring of diabetes-related ocular and cardiovascular complications. Informatics in Medicine Unlocked, 24.

Downloads

Published

2024-06-01

Issue

Section

Articles

How to Cite

Hassan, A. ., Khan, H. ., Ali, . A., din, I. U. ., Sajid, A. ., Husain, M. ., Ali, M. ., Naz, A. ., & Fakhar, H. . (2024). An Enhanced Lung Cancer Identification and Classification Based on Advanced Deep Learning and Convolutional Neural Network. Bulletin of Business and Economics (BBE), 13(2), 136-141. https://doi.org/10.61506/01.00308

Similar Articles

1-10 of 438

You may also start an advanced similarity search for this article.