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Related Experiment Video

Updated: Sep 18, 2025

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

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Efficient deep learning model for classifying lung cancer images using normalized stain agnostic feature method and

Pranshu Saxena1, Sanjay Kumar Singh2, Mamoon Rashid3

  • 1School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an AI system for lung cancer classification, achieving 99.78% accuracy. This computer-assisted diagnosis tool can enhance pathologist accuracy and efficiency in identifying non-small cell lung cancer.

Keywords:
ClassificationDeep learningDiagnosisHistopathological imagesLung cancer

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Area of Science:

  • Computational pathology
  • Medical imaging analysis
  • Artificial intelligence in oncology

Background:

  • Lung cancer is a leading cause of global mortality.
  • Accurate histological classification is crucial for effective treatment and patient outcomes.
  • Current diagnosis relies on manual analysis of tissue samples.

Purpose of the Study:

  • To develop a computer-assisted diagnosis (CADx) system for non-small cell lung cancer (NSCLC) histology classification.
  • To leverage deep learning and AI for improved diagnostic accuracy and efficiency.
  • To evaluate the performance of a modified ResNet-34 architecture.

Main Methods:

  • Utilized the FastAI-2 framework with a modified ResNet-34 deep learning architecture.
  • Implemented stain normalization in LAB color space for consistent color representation.
  • Trained the model on the LC25000 dataset and compared performance against VGG11 and SqueezeNet1_1.

Main Results:

  • The proposed modified ResNet-34 model achieved 99.78% accuracy in classifying lung cancer histology.
  • Demonstrated superior performance and an optimal balance between network depth and computational efficiency.
  • FastAI-2 framework facilitated rapid model convergence and reduced training time.

Conclusions:

  • Automated histopathology classification using AI shows high effectiveness for lung cancer diagnosis.
  • AI-driven tools can significantly assist pathologists by enhancing accuracy and reducing workload.
  • The developed system holds potential for improving clinical decision-making in oncology.