Efficacy of Machine Learning Models in Lung Cancer Detection: An Emphasis on Bees with ICA Hybrid Feature Extraction


  • Ashok Kumar Gottipalla Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India
  • Prasanth Yalla Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India


Bees Algorithm, Diagnostic paradigms, Hybrid feature reduction, Image processing techniques, Independent Component Analysis (ICA), Lung cancer diagnosis, Machine learning classifiers


The rapid and precise diagnosis of specific lung cancer types, including adenocarcinoma of the left lower lobe, large cell carcinoma of the left hilum, and squamous cell carcinoma of the left hilum, has become paramount in the realm of medical imaging. This research aims to harness advanced image processing techniques to extract disease-specific features and employ a hybrid algorithm for feature reduction, ultimately facilitating the accurate classification of these diseases. Features were meticulously extracted from medical images capturing the diseases above. A novel hybrid algorithm, which fuses the strengths of the Bees Algorithm and Independent Component Analysis (ICA), was introduced to address the challenge of high dimensionality. Following feature reduction, a battery of machine learning classifiers—including k-nearest Neighbours (kNN), Support Vector Machines (SVM), Logistic Regression, Linear Regression, and Random Forest—was applied to the curated features. The classifiers' performance metrics were rigorously evaluated, including accuracy, time complexity, precision, recall, and F1 score. Preliminary findings underscore the efficacy of the hybrid feature reduction technique in preserving salient disease markers, thus amplifying the classifiers' accuracy and computational efficiency. This study propounds a methodological advancement in detecting specific lung cancer types through image processing. The synergistic application of the hybrid feature reduction algorithm and machine learning classifiers offers promise in reshaping contemporary diagnostic paradigms, laying the groundwork for the next generation of diagnostic tools in lung cancer care.


Download data is not yet available.


Stocks, S. J., McNamee, R., Turner, S., Carder, M., & Agius, R. M. (2011, August 17). Has European Union legislation to reduce exposure to chromate in cement been effective in reducing the incidence of allergic contact dermatitis attributed to chromate in the UK? Occupational and Environmental Medicine, 69(2), 150–152. https://doi.org/10.1136/oemed-2011-100220

Zhu, Y., Zhao, Y., Cao, Z., Chen, Z., & Pan, W. (2022, April). Identification of three immune subtypes characterized by distinct tumor immune microenvironment and therapeutic response in stomach adenocarcinoma. Gene, 818, 146177. https://doi.org/10.1016/j.gene.2021.146177

Sato, T., & Date, H. (2017, March). Robot assisted left lower lobectomy, the case presented in Figure 1. Incomplete fissure between left upper and lower lobe was made after pulmonary artery and bronchus for left lower lobe had been divided. ASVIDE, 4, 78–78. https://doi.org/10.21037/asvide.2017.078

Schreiber, Y., & Berkovits, R. (2020, February 24). Entanglement between Distant Regions in Disordered Quantum Wires. Advanced Quantum Technologies, 3(4). https://doi.org/10.1002/qute.201900113

Hira, H. (2015). Blood Clot in Left Main Bronchus: A Treatable Cause of Left Lung Collapse. MAMC Journal of Medical Sciences, 1(1), 44. https://doi.org/10.4103/2394-7438.150064

Sabarish, R., & Ramadevi, R. (2023, February 14). Analysis and Comparison of Image Enhancement Technique for Improving PSNR of Lung Images by Median Filtering over Histogram Equalization Technique. CARDIOMETRY, 25, 818–824. https://doi.org/10.18137/cardiometry.2022.25.818824

Use of Statistical Techniques to Analyze Textures in Medical Images for Tumor Detection and Evaluation. (2019, January 4). Advanced Molecular Imaging and Interventional Radiology, 01–06. https://doi.org/10.33513/miir/1801-01

Analysis on Diagnosing Breast Cancer using Machine Learning Algorithms. (2020, November 2). International Journal of Pharmaceutical Research, 12(sp1). https://doi.org/10.31838/ijpr/2020.sp1.463

Vinny, P., Budhwar, V., Tyagi, R., & Hande, V. (2019). Epworth sleepiness score to predict sleep apnea in acute stroke: Do we need to delve deeper? Journal of Marine Medical Society, 21(1), 36. https://doi.org/10.4103/jmms.jmms_50_18

Yuqin Li, Y. L. (2021, August). Lung Fields Segmentation Based on Shape Compactness in Chest X-Ray Images. 電腦學刊, 32(4), 152–165. https://doi.org/10.53106/199115992021083204012

Agustina, D., Sari, D. P., Winanda, R. S., Hilmi, M. R., & Fakhriyana, D. (2022, June 30). Comparison of Portfolio Mean-Variance Method with the Mean-Variance-Skewness-Kurtosis Method in Indonesia Stocks. EKSAKTA: Berkala Ilmiah Bidang MIPA, 23(02), 88–97. https://doi.org/10.24036/eksakta/vol23-iss02/316

A, S., & C.M., V. (2022, April 24). A Contemporary Approach on Brain Tumor Edge Detection of Image Segmentation Using Log, Zero-Cross, and Canny Operators Comparing to Color Coding Technique for Efficient Discovery of Disease. ECS Transactions, 107(1), 14219–14232. https://doi.org/10.1149/10701.14219ecst

Zebari, R., Abdulazeez, A., Zeebaree, D., Zebari, D., & Saeed, J. (2020, May 15). A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction. Journal of Applied Science and Technology Trends, 1(2), 56–70. https://doi.org/10.38094/jastt1224

Cabrera, V. M. (2022, April 7). Updating the Phylogeography and Temporal Evolution of Mitochondrial DNA Haplogroup U8 with Special Mention to the Basques. DNA, 2(2), 104–115. https://doi.org/10.3390/dna2020008

Alzubaidi, M. A., Otoom, M., & Jaradat, H. (2021). Comprehensive and Comparative Global and Local Feature Extraction Framework for Lung Cancer Detection Using CT Scan Images. IEEE Access, 9, 158140–158154. https://doi.org/10.1109/access.2021.3129597




How to Cite

Gottipalla, A. K. ., & Yalla, P. . (2024). Efficacy of Machine Learning Models in Lung Cancer Detection: An Emphasis on Bees with ICA Hybrid Feature Extraction. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 31–40. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4780



Research Article

Most read articles by the same author(s)