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Lung Cancer Detection: A Classification Approach Utilizing Oversampling and Support Vector Machines

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Abstract

Lung cancer is the type of cancer that causes the most deaths each year. It is also cancer with the lowest survival rate. This represents a health problem worldwide. Lung cancer has two subtypes: Non-Small Cell Lung Cancer (NSCLC) and Small Cell Lung Cancer (SCLC). For doctors, it can be hard to detect and differentiate them. Therefore, in this work, we present a method to help doctors with this issue. It consists of three phases: image preprocessing is the first phase. It starts gathering the data. After that, PET scans are selected. Then, all the scans are converted to grayscale images, and finally, all the images are joined to create a video from each patient’s scan. Next, the data extraction phase starts. In this phase, some frames are extracted from each video, and they are flattened and blended to create a row of information from each frame. Thus, a dataframe is created where each row represents a patient, and each column is a pixel value. To obtain better results, an oversampling technique is applied. In this manner, the classes are balanced. Following this, a dimensionality reduction technique is applied to reduce the number of columns produced by the previous steps and to check if this technique improves the results yielded by each model. Subsequently, the model evaluation phase begins. At this stage, two models are created: a Support Vector Machine (SVM), and a Random Forest. Ultimately, the findings are unveiled, revealing that the SVM emerged as the top-performing model, boasting an impressive 97% accuracy, 98% precision, and 97% sensitivity. Eventually, this method can be applied to detect and classify different diseases that involve PET scans.

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Data availability

The dataset utilized in this research is sourced from "The Cancer Imaging Archive (TCIA)," accessible at https://imaging.cancer.gov/informatics/cancer_imaging_archive.htm. TCIA provides a comprehensive collection of openly available medical images, fostering collaborative research in cancer imaging.

Code Availability

The code implemented in this study is openly available on the following URL: https://colab.research.google.com/drive/1JKq3NV4wCkN1tTbwVkU-bNmL4qt2NPpy?usp=sharing. Researchers interested in reproducing or building upon the methods employed in this paper are encouraged to access the provided code. This transparency aims to facilitate the reproducibility of our findings and promote collaborative efforts in advancing cancer imaging research.

Notes

  1. More information can be found in: https://keras.io/examples/vision/video_classification/.

  2. For more information refer to: https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html.

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Acknowledgements

This research has been supported by the “Sistemas Inteligentes de Soporte a la Educación Especial (SINSAE v5)” research project of the UNESCO Chair on Support Technologies for Educational Inclusion.

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  1. GI-IATa, Cátedra UNESCO Tecnologías de apoyo para la Inclusión Educativa, Universidad Politécnica Salesiana, Cuenca, Ecuador

    Adolfo Jara-Gavilanes & Vladimir Robles-Bykbaev

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  1. Adolfo Jara-Gavilanes
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  2. Vladimir Robles-Bykbaev
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Correspondence to Vladimir Robles-Bykbaev.

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The authors of this paper declare that they have no conflicts of interest that could potentially influence or bias the interpretation of the research presented. There are no financial, personal, or professional relationships that might be perceived as having influenced the work reported in this manuscript. This includes, but is not limited to, financial interests, affiliations, or relationships with organizations that may have a direct or indirect interest in the subject matter discussed in the paper. We affirm that this work is conducted with integrity and in compliance with ethical standards. Any external factors that could pose a conflict of interest have been disclosed in an honest and transparent manner.

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This article is part of the topical collection “Emerging Technologies in Applied Informatics” guest edited by Hector Florez and Marcelo Leon.

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Jara-Gavilanes, A., Robles-Bykbaev, V. Lung Cancer Detection: A Classification Approach Utilizing Oversampling and Support Vector Machines. SN COMPUT. SCI. 5, 74 (2024). https://doi.org/10.1007/s42979-023-02432-6

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