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Novel Hybrid Quantum Architecture-Based Lung Cancer Detection Using Chest Radiograph and Computerized Tomography

Jason Elroy Martis1, Sannidhan M S2, Balasubramani R1

  • 1Department of ISE, NMAM Institute of Technology, Nitte Deemed to be University, Udupi 574110, Karnataka, India.

Bioengineering (Basel, Switzerland)
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This study introduces a hybrid deep learning and quantum computing framework for enhanced lung cancer detection from medical images. The novel approach achieves high accuracy, improving early diagnosis and patient care.

Keywords:
deep learning modelshybrid quantum layerlung tumor classificationquantum layerstransfer learning models

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Quantum Computing

Background:

  • Lung cancer is a leading global health challenge, necessitating improved early detection methods.
  • Current diagnostic tools require enhancement for accuracy and efficiency in identifying cancerous indicators.
  • Early detection significantly improves patient outcomes and treatment efficacy.

Purpose of the Study:

  • To develop and evaluate a hybrid framework combining deep learning (DL) and quantum computing for enhanced lung cancer detection.
  • To improve the accuracy and efficiency of identifying lung cancer from chest radiographs (CXR) and computerized tomography (CT) images.
  • To leverage quantum computing's capabilities for faster and more scalable cancer diagnosis.

Main Methods:

  • Utilized pre-trained deep learning models for feature extraction from CXR and CT images.
  • Employed quantum circuits for the classification of extracted features to detect lung cancer.
  • Integrated DL and quantum computing into a hybrid framework for a synergistic approach.

Main Results:

  • Achieved an overall accuracy of 92.12% in lung cancer detection.
  • Demonstrated high performance across key metrics: sensitivity (94%), specificity (90%), F1-score (93%), and precision (92%).
  • The hybrid approach showed superior accuracy and efficiency compared to traditional methods.

Conclusions:

  • The proposed hybrid DL and quantum computing framework offers a significant advancement in early lung cancer detection.
  • Quantum computing integration enhances processing speed and scalability, making it a promising tool for clinical screening.
  • This technology has the potential to transform cancer diagnostics, leading to improved patient care and outcomes.