Lung Cancer Detection and Classification Using Deep Learning

Ruchita Tekade, K. Rajeswari

International Conference on Computing Communication Control and automation 2018 conference-paper cited by 158

importance 7/10 ai assisted AI in cancer detection oncology imaging stage: research

Detecting lung nodules in CT scans and classifying their malignancy level at an early stage is difficult and important for diagnosis.

Early lung cancer detection directly affects patient survival outcomes.

A U-Net segments CT images and a proposed 3D multipath VGG-like network classifies nodules and malignancy, with predictions combined for final results.

95.60% accuracy and 0.387732 logloss on LIDC-IDRI, LUNA16 and Kaggle DSB 2017 datasets

High accuracy demonstrated on public datasets; no clinical deployment or external validation stated.

Venue International Conference on Computing Communication Control and automation
Published 2018
Work type conference-paper
Authors Ruchita Tekade, K. Rajeswari
Citations 158 (OpenAlex, at import time)
References 26
Model or method U-Net and 3D multipath VGG-like network
Organization not stated in source
Primary source https://doi.org/10.1109/iccubea.2018.8697352
Open access closed
Keywords lung cancer, CT scan, deep learning, U-Net, malignancy classification, CAD
7 / 10

significant — thousands to millions affected

Early lung cancer detection is called 'very important' in the abstract; no scale, clinical deployment, or external validation stated, but the disease domain implies major health impact.

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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