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Related Experiment Video

Updated: Jan 16, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Automated classification and explainable AI analysis of lung cancer stages using EfficientNet and gradient-weighted

Abdulmajeed Alqhatni1, T K S Rathish Babu2, T R Mahesh3

  • 1Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia.

Frontiers in Medicine
|September 26, 2025
PubMed
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A new deep learning model using EfficientNet-B0 achieves 99% accuracy in classifying lung cancer from CT scans. Explainable AI with Grad-CAM enhances transparency and reliability in automated cancer diagnosis.

Area of Science:

  • Oncology
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Accurate lung cancer staging from CT scans is crucial for patient prognosis and treatment planning.
  • Traditional manual interpretation of CT scans can be subjective and inconsistent.
  • Automated methods are needed to improve the accuracy and reliability of lung cancer classification.

Purpose of the Study:

  • To develop and evaluate an automated deep learning model for classifying lung cancer stages from CT images.
  • To enhance the interpretability and transparency of the deep learning model using Explainable AI (XAI) techniques.
  • To improve diagnostic accuracy and reliability in lung cancer detection.

Main Methods:

  • An EfficientNet-B0 based deep learning architecture was employed for image classification.
Keywords:
CT image classificationEfficientNetGradient-weighted class activation mapping (Grad-CAM)diagnostic imagingexplainable artificial intelligencelung cancer staging

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  • Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to provide visual explanations for model predictions.
  • The model was trained and validated using 1,190 CT scans from the IQ-OTH/NCCD dataset, categorized as benign, malignant, or normal.

    • Main Results:

    • The proposed model achieved high performance metrics: 99% accuracy, 99% precision.
    • Recall rates were 96% for benign, 99% for malignant, and 100% for normal cases.
    • Grad-CAM successfully identified critical regions in CT scans influencing classification, enhancing model interpretability.

    Conclusions:

    • The EfficientNet-B0 based deep learning model demonstrates exceptional performance in classifying lung cancer from CT scans.
    • The integration of Grad-CAM significantly improves the interpretability and trustworthiness of the automated diagnostic system.
    • This approach offers a reliable and transparent tool for medical image analysis in oncology, potentially aiding clinical decision-making.