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  1. Home
  3. Early Detection Of Lung Nodules Using A Revolutionized Deep Learning Model.
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  3. Early Detection Of Lung Nodules Using A Revolutionized Deep Learning Model.

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Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model.

Durgesh Srivastava1,2, Santosh Kumar Srivastava3, Surbhi Bhatia Khan4,5,6

  • 1Department of Computer Science and Engineering, Sharda School of Engineering and Technology, Sharda University, Greater Noida 201310, India.

Diagnostics (Basel, Switzerland)
|November 24, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

Early lung cancer detection is crucial for survival. This study introduces a Hybridized Faster R-CNN (HFRCNN) deep learning model that achieves over 97% accuracy in identifying lung cancer from medical images.

Keywords:
accuracybounding box regressiondetectionevaluationfuture pyramidal networkloss functionup-sampling

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in oncology
  • Deep learning for disease detection

Background:

  • Lung cancer is a leading global cause of cancer mortality.
  • Early detection significantly improves treatment outcomes and patient survival rates.
  • Deep learning (DL) algorithms show promise for identifying lung cancer in medical scans.

Purpose of the Study:

  • To develop and evaluate a Hybridized Faster R-CNN (HFRCNN) model for early lung cancer detection.
  • To assess the accuracy of HFRCNN in identifying lung nodules in medical images.
  • To compare the performance of HFRCNN against existing lung cancer detection methods.

Main Methods:

  • Utilized a two-stage, region-based entity detection approach (HFRCNN).
  • Employed a convolutional neural network (CNN) for classification and refinement of proposed regions.
  • Trained the HFRCNN model on a distinct dataset of medical images.

    • Main Results:

    • The HFRCNN model achieved a detection accuracy exceeding 97%.
    • Demonstrated superior performance compared to several previously reported methods.
    • Successfully identified potential indicators of lung cancer (lung nodules) in scanned images.

    Conclusions:

    • The proposed HFRCNN model offers a highly accurate method for early lung cancer identification.
    • This deep learning approach has the potential to significantly aid in the early diagnosis of lung cancer.
    • HFRCNN represents a valuable advancement in AI-driven medical image analysis for cancer detection.