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Updated: Sep 18, 2025

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
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Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative

Manal Abdullah Alohali1, Hamed Alqahtani2, Shouki A Ebad3

  • 1Department of Information Systems, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

Peerj. Computer Science
|June 26, 2025
PubMed
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This study introduces an optimized deep learning model for early lung cancer detection. The novel approach achieves 98.7% accuracy in classifying lung tumors, improving diagnostic reliability.

Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Lung cancer is a leading cause of death, often detected late due to diagnostic challenges.
  • Computer-aided diagnosis (CAD) systems show promise but face limitations like high dimensionality and overfitting.
  • Effective early detection is crucial for improving patient outcomes in lung cancer treatment.

Purpose of the Study:

  • To develop an optimized deep learning framework for accurate lung cancer classification.
  • To enhance early lung cancer detection by integrating advanced feature selection and classification techniques.
  • To overcome limitations of existing CAD systems in handling complex, high-dimensional lung cancer data.

Main Methods:

  • Data preprocessing involved Multiple Imputations by Chained Equations (MICE) and feature scaling.
Keywords:
Bidirectional generative adversarial networksBio-inspired algorithmsDeep learningFlying fox optimizationLung tumours

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  • Flying Fox Optimization (FFXO) was employed for efficient feature selection, reducing dimensionality.
  • Bidirectional Generative Adversarial Networks (Bi-GAN) were utilized for robust lung tumor classification.

    • Main Results:

    • The proposed system achieved a high accuracy of 98.7% on a public lung cancer dataset.
    • Evaluated using accuracy, precision, recall, and F1-score, the model outperformed conventional methods.
    • The FFXO algorithm effectively reduced feature dimensionality, enhancing classification efficiency.

    Conclusions:

    • The optimized deep learning approach demonstrates superior performance for precise lung tumor classification.
    • This novel framework offers improved reliability and efficiency in lung cancer detection.
    • The study presents a promising tool with significant potential for clinical application in early cancer diagnosis.