Table 10 Machine learning and sub-fields in lung cancer
Author/Ref. | Imaging Modality | Image Dataset Samples (with Classified Diseases) | ML Method | Performance Metrics(%) |
|---|---|---|---|---|
Sekeroglu et al. [154] | CT Scan | LIDC/IDRI – 100—Annotated Nodules 604 – Total Nodules & non-nodules (diameter ≥ 3 mm) | Multi-Perspective Hierarchical Deep Fusion Learning Approach | Accuracy – 91.20 Specificity – 87.00 Sensitivity – 95.00 False Positive/scan—0.4 |
Donga et al. [155] | CT Scan | LIDC/IDRI – 1018—Total | Modified Gradient Boosting Algorithm | Accuracy – 95.67 Precision – 95.70 Recall – 91.00 F1 Score – 94.10 |
Khehrah et al. [156] | CT Scan | LIDC – (~ 250–350)—Nodule’s Images of 70 lung Scans | Otsu method  +  SVM | Accuracy—92.00 Sensitivity – 93.75 Specificity – 91.18 Precision – 85.19 FPI – 0.13 FPE – 0.22 MCC – 0.8385 |
Ausawalaithong et al. [157] | X-Ray | JSRT – 100 – Malignant ( +) 147 – Benign and Normal (-) 247—Total ChestX-ray14 - 6,282 – Positive ( +) 1,05,197 – Negative (-) 1,11,479—Total | Transfer Learning - Base Model – Densenet-121 Retrained Model – A (On ChestX-ray14) Retrained Model – B (On JSRT) Retrained Model – C (On ChestX-ray14  + JSRT) | Retrained Model—C Mean - Accuracy—74.43 + 6.01 Specificity—74.96 + 9.85 Sensitivity – 74.68 + 15.33 |
Chen et al. [158] | CT Scan | 10,000—Total | Manual SegNet Deeplab v3 VGG 19 | Accuracy – 92.50 Sensitivity—98.33 Specificity – 86.67 Overlap Rate-95.11 |
Nanglia et al. [159] | Low-Dose CT Scan (LDCT) | 500—Total | Feature Extraction – SURF + Genetic Algorithm Classification -SVM + Feed Forward Back Propagation Neural Network | Overall Accuracy – 98.08 Precision—98.17 Recall—96.50 F-measure – 97.00 |
Alshmrani et al. [160] | X-ray | 20,000 – Lung Cancer 3,615 – COVID-19 5,856 – Pneumonia 6,012—Lung opacity 1,400 – Tuberculosis 10,192—Normal 80,000—Total | VGG19  +  3 Blocks of CNN | Accuracy – 96.48 Precision – 97.56 Recall – 93.75 F1 Score – 95.62 AUC – 99.82 |
Heuvelmans et al. [161] | CT Scan | NLST - 205—Malignant 2,106 – Total Lung Nodules | Lung Cancer Prediction CNN (LCP-CNN) | Sensitivity – 99.00 AUC—94.50 |
Rahouma et al. [162] | CT Scan | 30 – NSCLC 20 – Benign 50 – Total Lung Nodules | Polynomial Neural Network (PNN) | Accuracy—96.66 Sensitivity – 95.00 |
Bilal et al. [163] | X-ray | 250 – Normal 320 – Benign 320 – Malignant 910 – Total | VGGNet, ResNet, GoogLeNet AlexNet, InceptionNet-V3  + Improved Gray Wolf Optimization and InceptionNet-V3 | Accuracy – 98.96 Sensitivity—100.00 Specificity – 94.74 |
Torres et al. [164] | CT Scan | 09—Benign 51—Malignant 60 – Total Lung Nodules | Nodule Extraction – Otsu thresholding and morphological operations + GLCM + t-test Classification—Feed-Forward Neural Network | Accuracy – 96.30 Sensitivity—100.00 Specificity – 83.00 F1 Score – 97.67 AUC – 94.00 |
Hussain et al. [165] | MRI | 377 – NSCLC 568 – SCLC 945 – Total Lung Nodules | (I) Texture features using SVM polynomial (II) Image Adjustment using SVM RBF and Polynomial (III) Contrast stretching at threshold of (0.02, 0.98) using SVM RBF and Polynomial (IV) Gamma Correction at gamma value 0.9 | (I) Sensitivity = 100 Specificity = 99.72 Accuracy = 99.89 (II), (III), and (IV) - Sensitivity = 100 Specificity = 100 Accuracy = 100 |
Kuo et al. [166] | CT Scan | 273 – GGO 120 – Part Solid 274 – Solid 667 – Total Lung Nodules | Preprocessing – Adaptive Wiener filter Lung Segmentation—Fast Otsu & Edge Search Method Nodule Enhancement—Gray Level Adjustment Candidate Detection- Fast Otsu Method Classification—SVM | Total Sensitivity—92.05 Small Nodules (5 mm–9 mm) - Sensitivity—93.73 GGO – Sensitivity—93.02 |
Singh et al. [167] | CT Scan | 6,910 – Benign 8,840 – Malignant 15,750—Total Lung Nodules | Feature Extraction – GLCM + Statistical Method Classification -KNN, SVM, DT, RF, MLP, Naïve Bayes, Gradient Descent | Accuracy—88.55 Sensitivity – 89.84 Precision – 86.59 F1 Score – 87.35 |