Table 10.

Most commonly utilized machine learning classifiers for classifying nodules and cancer.

Model name	Purpose	Data type	Result	Strength	Limitation
RF [303]	Using pretrained model to detect lung cancer accurately	CT	Acc 82.5%	Improves the capacity of lung nodule prediction	Limited dataset and result
SVM [300]	Classifying the lung nodules in four lung cancer stages	CT	Acc 84.58%	Predicts small-sized lung nodules, even in low density	The limited dataset affected their results
LDA [301]	Classifying cancer using ODNN and LDA	CT	Acc 94.56%	It is quick, easy to use, non-invasive, and inexpensive	Optimal feature selection with multiclassifier was missing
RF [304]	Automatic classification of pulmonary peri-fissural nodules (PFNs)	CT	Sens 86.8%	Pretrained CNNs are employed, which makes them faster than training CNNs	All kinds of nodules were not classified
SVM [78]	To increase the accurate prediction of lung cancer	CT	Acc 85.7%	Predicts lung cancer from low-resolution data images	The model sometimes fails to predict
RF [299]	To detect malignancy of nodules with self-built model NoduleX	CT	Pres 99%	Solid, part-solid, and non-solid nodule categorization is performed automatically	Big nodules were accurately detected
RF [305]	Classified the measured solidity or nodules	CT	Acc 95%	Avoids potential errors caused by inaccurate image processing	The description of their work is not described clearly
SVM [306]	An improved FP-reduction method is used to detect lung nodules in PET/CT images	CT	Spec 97.2%	Removes around half of the existing FPs	Only small cohort is used
Boosting [307]	Classification of nodules with fusion of texture, shape, and deep model-learned data	CT	F1 96.65%	Generates more accurate outcomes than three existing state-of-the-art techniques	The model only detects big nodules
Multikernel learning [302]	Distinguishing between the nodule and non-nodule classes with classification	CT	Acc 94.17%	Increases the efficacy of false positive reduction	Dataset name is unclear
SVM [308]	Extracting absolute information inherent in raw hand-crafted imaging components	CT	Acc 95.5%	Obtains promising classification outcomes	The reference is limited
Decision tree [22]	Using autoencoder with decision tree to detect nodule	CT	Sens 75.01%	Outperforms the state-of-the-art techniques on the overall accuracy measure, even after experimenting with nearly five times the data amount	The results are low
SVM [309]	Nodule classification with hybrid features	CT	Acc 99.3%	It extracts the representative image of lung nodule malignancy from chest CT images	The model cannot detect type, position, and size
Decision tree [310]	Discovering radiomics to detect lung cancer	CT	Sens 77.52%	Increases the accuracy of lung cancer prediction diagnostics	The reference is limited and results are low
Boosting [66]	Identifying nodules from CT scan	CT	AUC 86.42%	Quickly finds the exact positions of latent lung nodule	The references of figure and table are accurately done
Multikernel learning [311]	To describe the algorithm for false positive reduction in lung nodule computer-aided detection (CAD)	CT	Jindex 91.39%	Automatically reduces unnecessary feature subsets to get a more discriminative feature set with promising classification performance	All false positive reduction is not done yet
Logistic regression [312]	Prediction of the malignancy of lung nodules in CT scans	CT	Sens 94.5%	Additional information based on nodule size has at best a mixed impact on classifier performance	It only takes large nodules
DBScan [68]	Detecting nodules with 3D DCNN	CT	Spec 79.67%	It can be expanded into other areas of medical image identification	FP reduction and automated classification are missing
Naïve Bayes [243]	A pretrained CNN to extract deep features from lung cancer images and train classifiers to predict all term survivors	CT	Acc 82.5%	The method's performance is such that adding nodule size information has only a mixed effect on classifier performance	The dataset was too small