Table 1 This table gives a summary of recent work that has been performed in lung cancer detection using machine learning and deep learning algorithms as discussed in Sect. 2.1
References | Feature extraction | Data | ML/DL | Acc (%) |
|---|---|---|---|---|
[15] 2018 | taxic weights, phylogenetic trees | LIDC-IDRI [23] | CNNs | 92.6 |
[16] 2018 | SCM | LIDC-IDRI [23] | MLP, \(k-\)NN, SVM | 96.7 |
[20] 2018 | histogram equalization | DenseNet | 74.4 | |
[25] 2019 | – | UCI [26] | SVM,LR,DT,Naive Bayes | 99.2 |
[18] 2019 | AdaBoost | ELVIRA biomedical data [27] | ANN | 99.7 |
[28] 2019 | UNet and ResNet | LIDC-IDRI [23] | XGBoost and RF | 84.0 |
[29] 2020 | – | spectroscopic data | ResNet | 95.0 |
[17] 2020 | HoG, LBP, SIFT, Zernike Moment | LIDC-IDRI [23] | FPSOCNN | 95.6 |
[30] 2021 | 2D-DFT and 2D-DWT | LC25000 images [24] | CNNs | 96.3 |
[19] 2021 | Correlation Attribute (CA) | UCI [26] | CNN, SVM, k-NN | 95.5 |
[31] 2022 | LeNet, AlexNet, VGG16, ResNet-50, Inception-V1 | LUNA16 [32] | Fully connected layer | 97.25 |