Table 3.
Summary of papers for lung cancer detection using deep learning.
| Reference | Deep Learning Technique | Features | Dataset |
|---|---|---|---|
| [13] | CNN | Features extracted from CNN | LUNA, LIDC, NLST |
| [113] | CNN with transfer learning | Features extracted from CNN | JSRT Dataset, NIH-14 dataset |
| [107] | Multi-stream multi-scale convolutional networks | Features extracted from CNN | MILD dataset DLCST dataset |
| [34] | CNN with transfer learning | Features extracted from CNN | NCI Genomic Data Commons |
| [110] | CNN with transfer learning and data augmentation | Features extracted from CNN | NSCLC-Radiomics, NSCLC-Radiomics-Genomics, RIDER Collections and several private datasets |
| [105] | CNN and DBN | Features extracted from CNN and DBN | LIDC-IDRI |
| [112] | CNN with transfer learning | Features extracted from CNN | Kaggle Data Science Bowl 2017 dataset, Lung Nodule Analysis 2016 (LUNA16) dataset |
| [25] | CNN | Features extracted from CNN | LIDC-IDRI |
| [108] | CNN | Features extracted from CNN | LIDC-IDRI |
| [23] | CNN with data augmentation | Features extracted from CNN | LIDC-IDRI database |
| [111] | CNN with transfer learning and data augmentation | Features extracted from CNN | Private dataset |
| [14] | Bone elimination and lung segmentation before training with CNN | Features extracted using CNN from bone eliminated lung images and segmented lung images | JSRT dataset |
| [114] | CNN-long short-term memory network | Features extracted from CNN | NIH-14 dataset |
| [109] | CNN with transfer learning and data augmentation | Features extracted from CNN | JSRT database |
| [106] | CNN with data augmentation | Features extracted from CNN | Cancer Imaging Archive |