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September 10, 2025JURNAL MASYARAKAT INFORMATIKAOpen Access

Regionprops Segmentation in Convolutional Neural Network for Identification of Lung Cancer Disease and Position

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Authors

ZSZahra Ghina SyafiraUniversitas Dian Nuswantoro CSChristy Atika SariUniversitas Dian Nuswantoro IMIbnu Utomo Wahyu MulyonoUniversitas Dian Nuswantoro

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Implication

This analysis demonstrates high accuracy in lung cancer identification using regionprops segmentation and CNN, suggesting promising diagnostic potential.

Key Points

The model with an 80:20 data ratio achieved an accuracy of 99.54%, showing excellent performance in lung cancer classification.
Using convolutional neural networks alongside regionprops segmentation successfully isolates cancer locations in CT scans.
Lung cancer diagnosis differentiates between benign, malignant, and normal classes across 1,294 CT scan images.
Combining CNN with regionprops segmentation enhances diagnostic accuracy, advocating for its application in medical imaging.

Cite This Study

Syafira et al. (2025) studied this question.

synapsesocial.com/papers/68c1a8f654b1d3bfb60e17dc https://doi.org/10.14710/jmasif.16.2.73967

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Implication

This analysis demonstrates high accuracy in lung cancer identification using regionprops segmentation and CNN, suggesting promising diagnostic potential.

Regionprops Segmentation in Convolutional Neural Network for Identification of Lung Cancer Disease and Position

Discussion

Member takes

Implication

Key Points

Cite This Study

Regionprops Segmentation in Convolutional Neural Network for Identification of Lung Cancer Disease and Position

Discussion

Member takes

Implication

Key Points

Cite This Study

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