Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Data Augmentation
2.3. Image Enhancement
2.3.1. Image Adjustment
2.3.2. Gamma Correction
2.3.3. Contrast Stretching
2.3.4. Thresholding
2.4. Feature Extraction
Gray-Level Co-Occurrence Matrix (GLCM)
2.5. Classification
2.5.1. Support Vector Machine (SVM)
2.5.2. Decision Tree (DTs)
2.5.3. Naïve Bayes (NB)
2.6. Training/Testing Data Formulation
2.7. Performance Evaluation
3. Results and Discussions
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
(GLCM) | Gray-level co-occurrence matrix |
(SVM) | Support Vector Machine |
(RBF) | Radial Base Function |
(DT) | Decision Tree |
(NSCLC) | Non-Small Cell Lung Cancer |
(SCLC) | Small Cell Lung Cancer |
(SBRT) | Stereotactic body radiotherapy |
(CT) | Computed Tomography |
(MR) | Magnetic Resonance |
(HE) | Histogram equalization |
(LCA) | Lung Cancer Alliance |
(DICOM) | Digital Imaging and Communications in Medicine |
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Method | Sensitivity | Specificity | PPV | NPV | Accuracy | FPR | AUC |
---|---|---|---|---|---|---|---|
Naïve Bayes | 0.8686 | 0.9101 | 0.9386 | 0.8141 | 0.8847 | 0.08989 | 0.98 |
Decision Tree | 0.9911 | 0.986 | 0.9911 | 0.986 | 0.9891 | 0.01404 | 0.98 |
SVM Gaussian | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
SVM RBF | 0.9982 | 1 | 1 | 0.9972 | 0.9989 | 0 | 1 |
SVM Poly. | 1 | 0.9972 | 0.9982 | 1 | 0.9989 | 0.002809 | 0.9999 |
Author | Features Used | Performance |
---|---|---|
Sousa et al. [69] | 1. Gradient 2. Histogram 3. Spatial | Sensitivity = 84%, Specificity = 96% Accuracy = 95% |
Dandil et al. [6] | 1. GLCM 2. Shape 3. Statistical 4. Energy | Sensitivity = 97%, Specificity = 94% Accuracy = 95% |
Nasrulla et al. [70] | 1. Statistical | Sensitivity = 94%, Specificity = 90% AUC = 0.990 |
Han et al. [71] | Machine learning and deep learning methods to distinguish SCLC types | Accuracy =84.10% |
Grossman et al. [72] | EfficientNet using transfer learning to distinguish NSCLC from SCLC | Accuracy = 90% |
Gao et al. [73] | Machine learning to classify subtypes of NSCLC | AUC = 0.972 |
Hussain et al. [13] | Texture features using MFE with standard deviation, Morphological features using RCMFE with mean EFDs features using MFE | p-value () p-value () p-value () |
This study | Texture features using SVM polynomial Image Adjustment using SVM RBF and Polynomial Contrast stretching at threshold of (0.02,0.98) using SVM RBF and Polynomial Gamma Correction at gamma value 0.9 | Sensitivity = 100%, Specificity = 99.72% Accuracy = 99.89 Sensitivity = 100%, Specificity. = 100% Accuracy = 100% Sensitivity = 100%, Specificity = 100% Accuracy = 100% Sensitivity = 100%, Specificity = 100% Accuracy = 100% |
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Hussain, L.; Alsolai, H.; Hassine, S.B.H.; Nour, M.K.; Duhayyim, M.A.; Hilal, A.M.; Salama, A.S.; Motwakel, A.; Yaseen, I.; Rizwanullah, M. Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features. Appl. Sci. 2022, 12, 6517. https://doi.org/10.3390/app12136517
Hussain L, Alsolai H, Hassine SBH, Nour MK, Duhayyim MA, Hilal AM, Salama AS, Motwakel A, Yaseen I, Rizwanullah M. Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features. Applied Sciences. 2022; 12(13):6517. https://doi.org/10.3390/app12136517
Chicago/Turabian StyleHussain, Lal, Hadeel Alsolai, Siwar Ben Haj Hassine, Mohamed K. Nour, Mesfer Al Duhayyim, Anwer Mustafa Hilal, Ahmed S. Salama, Abdelwahed Motwakel, Ishfaq Yaseen, and Mohammed Rizwanullah. 2022. "Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features" Applied Sciences 12, no. 13: 6517. https://doi.org/10.3390/app12136517