Deep learning for enhancing cancer identification and diagnosis

Research Article
Open access

Deep learning for enhancing cancer identification and diagnosis

Wenji Zuo 1*
  • 1 Southwest Jiaotong University    
  • *corresponding author el20w2z@leeds.ac.uk
Published on 11 December 2023 | https://doi.org/10.54254/2755-2721/27/20230093
ACE Vol.27
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-199-5
ISBN (Online): 978-1-83558-200-8

Abstract

One of the promising and significant fields of technology is the use of automated computer techniques, particularly machine learning, to facilitate and enhance medical analysis and diagnosis. In the area of artificial intelligence (AI), deep learning techniques using artificial neural networks (CNNs) – so-called because they superficially resemble biological neural networks - are computational network models for discovering large, high-dimensional data sets (such as medical datasets) for complex structures and patterns. In this paper, the focus is on summarizing contemporary applications of various deep learning algorithms in the direction of cancer identification and diagnosis, and on re-implementing supervised learning for cancer detection after a new database based on the lung cancer detection project from the book Deep Learning with PyTorch. The automatic detection of lung malignancies from patient CT scans was re-implemented by deep learning. In this paper, the technique is applied to data provided by the Iraqi Oncology Teaching Hospital. The author demonstrates that the application of the new data will also maintain the accuracy of the identification, and thus promises to develop into a more comprehensive and general cancer detection and diagnosis method in the future as the algorithm and technology are further refined.

Keywords:

deep learning, cancer identification, PyTorch, LUNA model

Zuo,W. (2023). Deep learning for enhancing cancer identification and diagnosis. Applied and Computational Engineering,27,199-205.
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References

[1]. Cancer facts & figures 2022 (no date) American Cancer Society. Available at: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html (Accessed: January 1, 2023).

[2]. François, C. Deep learning with python. Shelter Island: Manning (2021).

[3]. Munir, K. et al. Cancer diagnosis using Deep Learning: A bibliographic review, MDPI. Multidisciplinary Digital Publishing Institute (2019). Available at: https://www.mdpi.com/2072-6694/11/9/1235#metrics (Accessed: November 5, 2022).

[4]. Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D. M. L. D. and Silva, C. A. "Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3037-3040 (2015). DOI: 10.1109/EMBC.2015.7319032.

[5]. Tustison, N. J., Shrinidhi, K., Wintermark, M., Durst, C. R., Kandel, B. M., Gee, J. C., Grossman, M. C., Avants, B. B. Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13, 209-225 (2015). Available at: [Google Scholar] (Accessed: November 20, 2022).

[6]. Munir, K. et al. Cancer diagnosis using Deep Learning: A bibliographic review, MDPI. Multidisciplinary Digital Publishing Institute (2019). Available at: https://www.mdpi.com/2072-6694/11/9/1235 (Accessed: November 20, 2022).

[7]. Xu, B. et al. Empirical evaluation of rectified activations in the convolutional network, arXiv.org. (2015). Available at: https://arxiv.org/abs/1505.00853 (Accessed: October 23, 2022).

[8]. Echle, A. et al. Deep learning in cancer pathology: A new generation of clinical biomarkers, Nature News. Nature Publishing Group (2020). Available at: https://www.nature.com/articles/s41416-020-01122-x (Accessed: 2022).

[9]. Using deep learning to enhance cancer diagnosis and classification (no date). Available at: https://www.researchgate.net/publication/281857285_Using_deep_learning_to_enhance_cancer_diagnosis_and_classification (Accessed: January 8, 2023).

[10]. Wang, D. et al. Deep learning for identifying metastatic breast cancer, arXiv.org. (2016). Available at: https://arxiv.org/abs/1606.05718.

[11]. Hu, Z. L., Tang, J. S., Wang, Z. M., Zhang, K., Zhang, L. and Sun, Q. L. Deep learning for image-based cancer detection and diagnosis − A survey, Pattern Recognition 83, 134-149 (2018). ISSN 0031-3203. https://doi.org/10.1016/j.patcog.2018.05.014.

[12]. LeCun, Y., Bengio, Y. and Hinton, G. Deep learning, Nature News. Nature Publishing Group (2015). Available at: https://www.nature.com/articles/nature14539 (Accessed: November 5, 2022).

[13]. Stevens, E. et al. Deep learning with PyTorch. Shelter Island: Manning Publications (2020).

[14]. U-Net: Convolutional Networks for Biomedical Image Segmentation (no date). Available at: https://arxiv-export-lb.library.cornell.edu/pdf/1505.04597 (Accessed: December 12, 2023).

[15]. Mahimkar, A. IQ-oth/NCCD-lung cancer dataset, Kaggle (2021). Available at: https://www.kaggle.com/datasets/adityamahimkar/iqothnccd-lung-cancer-dataset (Accessed: November 5, 2022).

Cite this article

Zuo,W. (2023). Deep learning for enhancing cancer identification and diagnosis. Applied and Computational Engineering,27,199-205.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-83558-199-5(Print) / 978-1-83558-200-8(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.27
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Cancer facts & figures 2022 (no date) American Cancer Society. Available at: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html (Accessed: January 1, 2023).

[2]. François, C. Deep learning with python. Shelter Island: Manning (2021).

[3]. Munir, K. et al. Cancer diagnosis using Deep Learning: A bibliographic review, MDPI. Multidisciplinary Digital Publishing Institute (2019). Available at: https://www.mdpi.com/2072-6694/11/9/1235#metrics (Accessed: November 5, 2022).

[4]. Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D. M. L. D. and Silva, C. A. "Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 3037-3040 (2015). DOI: 10.1109/EMBC.2015.7319032.

[5]. Tustison, N. J., Shrinidhi, K., Wintermark, M., Durst, C. R., Kandel, B. M., Gee, J. C., Grossman, M. C., Avants, B. B. Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 13, 209-225 (2015). Available at: [Google Scholar] (Accessed: November 20, 2022).

[6]. Munir, K. et al. Cancer diagnosis using Deep Learning: A bibliographic review, MDPI. Multidisciplinary Digital Publishing Institute (2019). Available at: https://www.mdpi.com/2072-6694/11/9/1235 (Accessed: November 20, 2022).

[7]. Xu, B. et al. Empirical evaluation of rectified activations in the convolutional network, arXiv.org. (2015). Available at: https://arxiv.org/abs/1505.00853 (Accessed: October 23, 2022).

[8]. Echle, A. et al. Deep learning in cancer pathology: A new generation of clinical biomarkers, Nature News. Nature Publishing Group (2020). Available at: https://www.nature.com/articles/s41416-020-01122-x (Accessed: 2022).

[9]. Using deep learning to enhance cancer diagnosis and classification (no date). Available at: https://www.researchgate.net/publication/281857285_Using_deep_learning_to_enhance_cancer_diagnosis_and_classification (Accessed: January 8, 2023).

[10]. Wang, D. et al. Deep learning for identifying metastatic breast cancer, arXiv.org. (2016). Available at: https://arxiv.org/abs/1606.05718.

[11]. Hu, Z. L., Tang, J. S., Wang, Z. M., Zhang, K., Zhang, L. and Sun, Q. L. Deep learning for image-based cancer detection and diagnosis − A survey, Pattern Recognition 83, 134-149 (2018). ISSN 0031-3203. https://doi.org/10.1016/j.patcog.2018.05.014.

[12]. LeCun, Y., Bengio, Y. and Hinton, G. Deep learning, Nature News. Nature Publishing Group (2015). Available at: https://www.nature.com/articles/nature14539 (Accessed: November 5, 2022).

[13]. Stevens, E. et al. Deep learning with PyTorch. Shelter Island: Manning Publications (2020).

[14]. U-Net: Convolutional Networks for Biomedical Image Segmentation (no date). Available at: https://arxiv-export-lb.library.cornell.edu/pdf/1505.04597 (Accessed: December 12, 2023).

[15]. Mahimkar, A. IQ-oth/NCCD-lung cancer dataset, Kaggle (2021). Available at: https://www.kaggle.com/datasets/adityamahimkar/iqothnccd-lung-cancer-dataset (Accessed: November 5, 2022).

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