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Related Experiment Video

Updated: Jul 23, 2025

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Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon

Rayed AlGhamdi1, Turky Omar Asar2, Fatmah Y Assiri3

  • 1Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Cancers
|July 14, 2023
PubMed
Summary

This study introduces a new method for detecting lung and colon cancer (LCC) using histopathological images. The Al-Biruni Earth Radius Optimization with Transfer Learning (BERTL-HIALCCD) technique improves early cancer diagnosis accuracy.

Keywords:
computer-aided diagnosislung and colon cancermedical image analysisparameter optimizationtransfer learning

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Artificial Intelligence in Medicine

Background:

  • Early diagnosis of lung and colon cancer (LCC) is crucial for patient outcomes.
  • Histopathological image (HSI) analysis is vital for LCC diagnosis but is time-consuming and prone to human error.
  • Computer-aided methods, particularly transfer learning (TL), offer a solution for automated HSI analysis.

Purpose of the Study:

  • To develop and evaluate the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique.
  • To achieve accurate and efficient detection of LCC in histopathological images.

Main Methods:

  • The BERTL-HIALCCD technique integrates computer vision and transfer learning.
  • An improved ShuffleNet model, with hyperparameters optimized by the Al-Biruni Earth Radius (BER) system, is used for feature extraction.
  • A deep convolutional recurrent neural network (DCRNN) is employed for LCC recognition, with parameters tuned by the coati optimization algorithm (COA).

Main Results:

  • Experimental validation on a large HSI dataset demonstrated the efficacy of the BERTL-HIALCCD technique.
  • The combined Al-Biruni Earth Radius (AER) and COA algorithms achieved superior performance in cancer detection compared to existing models.

Conclusions:

  • The BERTL-HIALCCD technique provides an effective computer-aided approach for LCC detection in histopathological images.
  • This method shows significant potential for improving the accuracy and efficiency of early cancer diagnosis.