Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features

Hussain, Lal; Aziz, Wajid; Alshdadi, Abdulrahman A.; Nadeem, Malik Sajjad Ahmed; Khan, Ishtiaq Rasool; Chaudhry, Qurat-Ul-Ain

doi:10.1109/access.2019.2917303

Cited by 30 publications

(12 citation statements)

References 110 publications

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“…In previous studies, numerous researchers have extracted many features for detecting various imaging pathologies by considering texture, shape-based morphologies, and image scaling and rotation changes and complex dynamics using SIFT, morphological, textural, EFDs and some other most relevant features regarding the nature of the problem of interest 4 , 5 , 7 , 47 , 48 . The feature extracted developed and employed in our previous studies are detailed in 7 , 49 – 53 . In this study, we first computed the Gray-level co-occurrence matrix-based texture features.…”

Section: Methodsmentioning

confidence: 99%

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Hussain

Malibari

Alamgeer

et al. 2022

Sci Rep

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Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.

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Section: Methodsmentioning

confidence: 99%

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Hussain

Malibari

Alamgeer

et al. 2022

Sci Rep

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“…Grossman et al [72] applied EfficientNet to deep learning, and obtained a highest accuracy of 90%. Hussain et al [13] computed different entropic-based features and computed the nonlinear dynamics to distinguish the SCLC from NSCLC with the highest significant results (p-value < 0.000000). In this study, we first applied image enhancement methods, such as gamma correction at different gamma values, contrast stretching at different thresholds, image adjustment, and histogram processing methods, and then computed the GLCM texture features.…”

Section: Resultsmentioning

confidence: 99%

“…The same dataset is utilized and detailed in [13]. We utilized 10-fold cross validation, which minimizes the chances of overfitting [14].…”

Section: Datasetmentioning

confidence: 99%

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

et al. 2022

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In the present era, cancer is the leading cause of demise in both men and women worldwide, with low survival rates due to inefficient diagnostic techniques. Recently, researchers have been devising methods to improve prediction performance. In medical image processing, image enhancement can further improve prediction performance. This study aimed to improve lung cancer image quality by utilizing and employing various image enhancement methods, such as image adjustment, gamma correction, contrast stretching, thresholding, and histogram equalization methods. We extracted the gray-level co-occurrence matrix (GLCM) features on enhancement images, and applied and optimized vigorous machine learning classification algorithms, such as the decision tree (DT), naïve Bayes, support vector machine (SVM) with Gaussian, radial base function (RBF), and polynomial. Without the image enhancement method, the highest performance was obtained using SVM, polynomial, and RBF, with accuracy of (99.89%). The image enhancement methods, such as image adjustment, contrast stretching at threshold (0.02, 0.98), and gamma correction at gamma value of 0.9, improved the prediction performance of our analysis on 945 images provided by the Lung Cancer Alliance MRI dataset, which yielded 100% accuracy and 1.00 of AUC using SVM, RBF, and polynomial kernels. The results revealed that the proposed methodology can be very helpful to improve the lung cancer prediction for further diagnosis and prognosis by expert radiologists to decrease the mortality rate.

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“…The dataset utilized in this study, provided by Lung Cancer Alliance (LCA) can be obtained at request on their official website (https://www.prnewswire.com/news-releases/lung-cancer-alliance-launches-first-open-access-patient-driven-websitefor-ct-scans-and-clinical-data-95842964.html). This dataset is utilized and detailed previously by [23] and similar other studies. The database images are in the Digital Imaging and Communications in Medicin (DICOM) format with total 76 patients with a total of 945 images including 377 images of NSCLC and 568 of SCLC subjects.…”

Section: 1datasetsmentioning

confidence: 99%

Improving Lungs Cancer Detection Based on Hybrid Features and Employing Machine Learning Techniques

Yang

Por

Khan

et al. 2023

Preprint

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Lung cancer detection using machine learning involves training a model on a dataset of medical images, such as CT scans, to identify patterns and features associated with lung cancer. Past researchers developed different computer aided diagnostic (CAD) systems for early prediction of lung cancer. The researchers extracted single features such as texture, morphology etc.; however, by combining the features, accuracy can be improved. In this study, we extracted Gray-level co-occurrence (GLCM), autoencoder and Haralick texture features. We combined these features and computed the performance using robust machine algorithms including Decision tree (DT), Naïve Bayes (NB) and support vector machine (SVM) with different kernel functions. The performance was evaluated using standard performance measures. The hybrid methods such as GLCM + Autoencoder, and Haralick + Autoencoder yielded highest detection performance using SVM Gaussian and radial base function (RBF) with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) with accuracy of 100% and AUC 1.00 followed by SVM polynomial yielded an accuracy of 99.89% and AUC of 1.00; GLCM + Haralick using SVM Gaussian yielded accuracy (99.56%), SVM RBF yielded accuracy (99.35%). The results reveal that the proposed feature extraction methodology can be usefully used to predict the lung cancer for further diagnosis at early stage. MSC: Artificial Intelligence, Machine Learning, Lung Cancer, cross validation

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Analyzing the Dynamics of Lung Cancer Imaging Data Using Refined Fuzzy Entropy Methods by Extracting Different Features

Cited by 30 publications

References 110 publications

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Lung Cancer Prediction Using Robust Machine Learning and Image Enhancement Methods on Extracted Gray-Level Co-Occurrence Matrix Features

Improving Lungs Cancer Detection Based on Hybrid Features and Employing Machine Learning Techniques

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