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EfficientNetB7-Based Deep Learning Framework for Enhanced Classification of Lung and Colon Cancer Histopathological

Pai H Aditya1, T R Mahesh2, J V Muruga Lal Jeyan1

  • 1Department of CSE, MIT School of Computing, MIT Art, Design and Technology University.

Journal of Visualized Experiments : Jove
|February 23, 2026
PubMed
Summary

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This summary is machine-generated.

A new deep learning model, EfficientNetB7, accurately classifies lung and colon tissues for early cancer detection. This artificial intelligence (AI) approach enhances diagnostic speed and effectiveness, improving patient survival rates.

Area of Science:

  • Pathology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Early diagnosis of lung cancer is critical for patient treatment and survival.
  • Artificial intelligence (AI) is revolutionizing pathology, enhancing diagnostic accuracy and efficiency.

Purpose of the Study:

  • To develop and validate a robust deep learning model for classifying histopathological images of colon and lung tissues.
  • To improve the accuracy and efficiency of cancer diagnosis in pathology.

Main Methods:

  • Utilized a pretrained EfficientNetB7 deep learning model.
  • Employed advanced preprocessing, fine-tuning, and domain-specific data augmentation.
  • Incorporated techniques to mitigate class imbalance, histological variations, and overfitting, including early stopping.

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Main Results:

  • Achieved an exceptionally high accuracy of 96% in classifying colon and lung tissue histopathological images.
  • Demonstrated robust validation, indicating high utility for clinical applications.

Conclusions:

  • The EfficientNetB7 deep learning model offers a promising tool for timely and accurate cancer diagnosis.
  • Integration of AI in medical imaging workflows can significantly improve patient outcomes through early cancer detection.