Automatic Lung Cancer Prediction from Chest X-ray Images Using the Deep Learning Approach

Worawate Ausawalaithong, Arjaree Thirach, Sanparith Marukatat, Theerawit Wilaiprasitporn

Biomedical Engineering International Conference 2018 conference-paper cited by 168

importance 8/10 ai assisted AI in cancer detection oncology imaging stage: research

Rural areas lack access to CT screening, so lung cancer classification from widely available chest X-rays was needed.

Lung cancer is curable when diagnosed early, but CT access is limited in rural areas.

A 121-layer DenseNet-121 convolutional network with transfer learning, pre-trained on a lung nodule dataset, classified lung cancer from chest X-ray images.

Mean accuracy 74.43±6.01%, specificity 74.96±9.85%, sensitivity 74.68±15.33%

Demonstrates feasibility of CNN-based lung cancer classification from chest X-rays at modest accuracy.

Venue Biomedical Engineering International Conference
Published 2018
Work type conference-paper
Authors Worawate Ausawalaithong, Arjaree Thirach, Sanparith Marukatat, Theerawit Wilaiprasitporn
Citations 168 (OpenAlex, at import time)
References 22
Model or method DenseNet-121 with transfer learning
Organization not stated in source
Primary source https://doi.org/10.1109/bmeicon.2018.8609997
Open access green
Keywords deep learning, DenseNet-121, chest X-ray, lung cancer, transfer learning
8 / 10

major — millions of lives, an industry or a field

Early-stage lung cancer is curable and screening matters, but CT/LDCT access is very limited in rural areas; abstract gives no patient numbers; large-scale cancer screening problem inferred.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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