What question did this study set out to answer?

The aim is to improve the early detection of lung cancer using deep learning techniques for classifying lung CT images.

August 2, 2026Scientific ReportsOpen Access

Deep learning driven early detection of lung cancer from CT images using transfer learning approaches

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Authors

VPVaishnavi PatangeDr. Babasaheb Ambedkar Technological University JJJagadish B. JadhavShri Vile Parle Kelavani Mandal SNSanjay L. NalbalwarDr. Babasaheb Ambedkar Technological University

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Implication

Randomized trial demonstrates automated classification of lung CT images for early lung cancer detection, suggesting significant diagnostic potential.

Key Points

The aim is to improve the early detection of lung cancer using deep learning techniques for classifying lung CT images.
Utilized VGG16 model optimized via transfer learning on the IQ-OTH/NCCD lung cancer dataset.
Implemented data acquisition, preprocessing, and performance evaluation with patient-level data splitting.
Conducted model training and validation with comparative analysis across six architectures.
VGG16 achieved mean test accuracy of 89.39% ± 2.10% and test loss of 0.329.
Significant performance improvement over ResNet50 and EfficientNetB0 confirmed by ANOVA (F = 17.55, p < 0.0001).
Grad-CAM visualizations indicated model focus on anatomically relevant lung regions.

Cite This Study

Patange et al. (2026) studied this question.

synapsesocial.com/papers/6a6eeae61b0468a7eeab3858 https://doi.org/10.1038/s41598-026-64739-7

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Implication

Randomized trial demonstrates automated classification of lung CT images for early lung cancer detection, suggesting significant diagnostic potential.

Deep learning driven early detection of lung cancer from CT images using transfer learning approaches

Discussion

Member takes

Implication

Key Points

Cite This Study

Deep learning driven early detection of lung cancer from CT images using transfer learning approaches

Discussion

Member takes

Implication

Key Points

Cite This Study

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