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Lung Cancer Classification in Histopathology Images Using Multiresolution Efficient Nets.

Sunila Anjum1, Imran Ahmed2, Muhammad Asif3

  • 1Center of Excellence in Information Technology, Institute of Management Sciences, Hayatabad, Peshawar 25000, Pakistan.

Computational Intelligence and Neuroscience
|October 25, 2023
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Summary
This summary is machine-generated.

This study introduces an automated deep learning method using EfficientNet variants to classify cancer in histopathological images. EfficientNetB2 achieved 97% accuracy, offering a faster, more precise diagnostic tool for pathologists.

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Area of Science:

  • Digital pathology and computational imaging.
  • Artificial intelligence in medical diagnostics.
  • Cancer research and histopathology analysis.

Background:

  • Histopathological images are crucial for disease diagnosis, including cancer.
  • Digital histopathology enhances diagnostic precision and pathologist efficiency.
  • Manual image analysis is time-consuming, necessitating automated solutions.

Purpose of the Study:

  • To develop and evaluate a deep learning approach for automated classification of normal and malignant cancer histopathological images.
  • To compare the performance of EfficientNet variants (B0-B7) for this classification task.
  • To investigate the impact of different image resolutions and transfer learning on model accuracy.

Main Methods:

  • Application of EfficientNet deep learning models (B0-B7) with varying image resolutions (224x224 to 600x600 pixels).
  • Utilized transfer learning and parameter tuning to optimize model performance and mitigate overfitting.
  • Employed the Lung and Colon Cancer Histopathological Image LC25000 dataset (25,000 images across five classes).
  • Performed data preprocessing to ensure image quality and standardization.

Main Results:

  • All EfficientNet variants demonstrated good classification accuracy.
  • EfficientNetB2 achieved excellent performance with 97% accuracy using 260x260 pixel images.
  • The study confirmed the effectiveness of deep learning for histopathological image analysis.

Conclusions:

  • Deep learning, specifically EfficientNet models, provides a reliable automated method for classifying cancer in histopathological images.
  • EfficientNetB2 shows significant promise for improving diagnostic speed and accuracy in digital pathology.
  • Automated analysis can enhance treatment outcomes by enabling faster therapeutic interventions.