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Artificial intelligence for detection of lung cancer using transfer learning and morphological features

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Abstract

Lung cancer is an uncontrolled growth of tissue causing a lump in the human lung. If lung cancer can be detected early, it can increase the survival rate. Therefore, a multi-classification approach of lung nodule detection with high computational effectiveness is required. In this paper, a multi-classification approach of lung nodule detection and classification is proposed using artificial intelligence on computed tomography (CT) scan images. Different preprocessing steps are applied for resizing, smoothing, and enhancement of the CT images. Then, two different approaches for feature extraction using VGG16 transfer learning and morphological segmentation are proposed. Morphological segmentation and feature extraction are applied for the segmentation of the region of interest and to extract the distinct features. Finally, the proposed deep learning architecture and seven different machine learning algorithms are applied on the preprocessed data and the extracted features for the classification of lung nodules into three classes: malignant, benign, and normal. It is observed that the stacked ensemble model of deep learning convolutional neural network (CNN) and VGG16 transfer learning models (CNN+VGG16) can achieve 99.55% accuracy using preprocessed data. It is also observed that all the ML algorithms perform with reasonably high accuracy using the low-dimensional morphological features. It is observed from the fivefold cross-validation results that logistic regression performs with 99.36% accuracy in 23.71 s time using the preprocessed data. Whereas, using the morphological features, k-nearest neighbor, and the support vector machine perform with the highest accuracy of 99.76% with very reduced computational time of 0.017 and 0.008 s, respectively.

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Availability of supporting data

The IQ-OTH/NCCD Lung Cancer Dataset are available at the Mendeley Data repository and the Kaggle data archive.

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Acknowledgements

We thank the contributor of the dataset available at the Mendeley Data repository and the IQ-OTHNCCD lung cancer dataset archive of Kaggle. We also thank the Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology for providing the necessary resources to conduct the research.

Funding

The research is supported by the AUST Internal Research Grant (Round 3) of Ahsanullah University of Science and Technology, Bangladesh.

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Author notes

  1. Nafe Muhtasim and Umma Hany have contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

    Nafe Muhtasim, Umma Hany, Tahmina Islam, Nusrat Nawreen & Abdullah Al Mamun

Authors
  1. Nafe Muhtasim
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  2. Umma Hany
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  3. Tahmina Islam
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  4. Nusrat Nawreen
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  5. Abdullah Al Mamun
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Contributions

NMH developed the deep learning CNN architecture, the transfer learning VGG16 model, and the stacked ensemble model, applied preprocessing, deep learning, and machine learning classifiers, and prepared Figures 10, 12–17. UH developed the morphological segmentation and feature extraction, applied preprocessing, applied machine learning classifier, and stacked ensemble model, prepared Figures 1–9, 11, 18, contributed to the literature review, and wrote the manuscript. TI wrote the "Machine learning classifiers" subsection and the "Performance Evaluation" section, contributed to the literature review, and reviewed the manuscript. NN developed five morphological operations in Matlab and contributed to the literature review. AAM collected datasets from Kaggle and reviewed the manuscript.

Corresponding author

Correspondence to Umma Hany.

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Conflict of interest

The authors declare no conflict of interest.

Ethical approval

We have used lung cancer datasets collected in the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) over a period of three months. It includes CT scans of patients diagnosed with lung cancer in different stages, as well as healthy subjects. The IQ-OTH/NCCD slides were marked by oncologists and radiologists. The source of the collected IQ-OTH/NCCD lung cancer dataset has been cited in this paper.

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Muhtasim, N., Hany, U., Islam, T. et al. Artificial intelligence for detection of lung cancer using transfer learning and morphological features. J Supercomput 80, 13576–13606 (2024). https://doi.org/10.1007/s11227-024-05942-z

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