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Hybrid active shape model and deep neural network approach for lung cancer detection

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Abstract

Lung cancer is consistently ranked as the primary cause of cancer-related fatalities worldwide. The timely identification and effective treatment of lung cancer play a pivotal role in patient survival rates. Generally, higher rates of lung cancer mortality have been observed in men compared to women, largely attributable to smoking levels. This article proposes a new hybrid approach to lung cancer detection using the Computed Tomography (CT) scan images. Our objective is two folds: first, the development of a robust and accurate segmentation approach based on the Active Shape Model (ASM), and second, the implementation of a fully automatic lung cancer detection system employing the Deep Neural Networks (DNN). Given the diverse nature of cancer growth within the lung, it can appear in any location, showing a wide range of shapes, sizes, and contrasts. The proposed approach thus lays the foundation for precise segmentation, enabling a comprehensive understanding of the structural nuances. The experimental evaluation shows that the proposed approach achieves good precision and accuracy and can help practitioners as an enhanced tool for fast and reliable cancer detection.

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Author information

Authors and Affiliations

  1. Computer Science Department, Faculty of Sciences, Gafsa University, Gafsa, Tunisia

    Mohamed Othmani

  2. Unit of Scientific Research, Applied College, Qassim University, Buraydah, Saudi Arabia

    Brahim Issaoui

  3. Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia

    Salim El Khediri & Rehan Ullah Khan

Authors
  1. Mohamed Othmani
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  2. Brahim Issaoui
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  3. Salim El Khediri
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  4. Rehan Ullah Khan
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Correspondence to Salim El Khediri.

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Othmani, M., Issaoui, B., El Khediri, S. et al. Hybrid active shape model and deep neural network approach for lung cancer detection. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01853-7

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