A comparative study for brain tumor detection in MRI images using texture features

Bhagat, P. K.; Choudhary, Prakash; Singh, Kh. Manglem

doi:10.1016/b978-0-12-819361-7.00013-0

Cited by 26 publications

(20 citation statements)

References 34 publications

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“…The LBP method extracts and encodes local texture details from images by comparing each pixel with its surrounding neighborhood and using the binary encoding. The LBP method has a low computation cost and handles the variations in the images' intensity values, which makes it particularly used in medical image processing 24–27 . The LBP texture features are calculated as follows: The image is divided into a number of cells that have a center pixel

g\mathrm{c}\ \left({gc}_x,{gc}_y\right)

. For each cell, establish a circular neighborhood

P

with a radius

R

and a center pixel

gc

and compute the coordinates of each pixel

gp

within the neighborhood along a clockwise or counterclockwise using Equation ().…”

Section: Methodsmentioning

confidence: 99%

Breast cancer: Three‐class masses classification in mammograms using Apriori dynamic selection

Soulami

Kaabouch

Saïdi

2022

Concurrency and Computation

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Mammography is a commonly used screening technique for early diagnosis of breast cancer. However, the early detection of abnormalities remains challenging, particularly for dense breast categories. In this context, the automated classification of breast masses assists radiologists in their diagnosis and give them a second opinion. In this paper, we propose a machine learning-based method for the classification of breast masses. First, the shape and texture features are extracted from the suspicious mammogram patches. These features are then fed to the Principal Component Analysis (PCA) to keep the relevant features only and are classified using the Support Vector Machine (SVM) and Random Forest (RF). Lastly, the Apriori dynamic selection method is applied for the final test predictions using the appropriate classifier for each test sample. The classification of breast masses patches into normal and abnormal attains accuracy of 96.43%, F1-score of 95.76%, precision of 96.29%, recall of 95.27%, specificity of 96.43%, and AUC of 0.963. Whereas the one-stage multi-classification of breast masses into normal, benign, and malignant achieves accuracy of 75.81%,

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g\mathrm{c}\ \left({gc}_x,{gc}_y\right)

. For each cell, establish a circular neighborhood

P

with a radius

R

and a center pixel

gc

and compute the coordinates of each pixel

gp

within the neighborhood along a clockwise or counterclockwise using Equation ().…”

Section: Methodsmentioning

confidence: 99%

Breast cancer: Three‐class masses classification in mammograms using Apriori dynamic selection

Soulami

Kaabouch

Saïdi

2022

Concurrency and Computation

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“…Texture analysis focuses on finding a specific way of representing the hidden characteristics of textures and express them in a simplified and unique form. Grey level co-occurrence matrices (GLCM) of MRI ADC images can be identified as a rich source of statistical texture features which can be utilized in training robust machine learning (ML) models, which is a powerful method that is commonly utilize to identify the unique patterns of the distribution of texture features within an image [20] [20,21,22].…”

Section: Texture Featuresmentioning

confidence: 99%

Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques

Vijithananda

Jayatilake

Hewavithana

et al. 2022

Preprint

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Background: Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.Methods: This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients.The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient.At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.Results: According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.Conclusion: This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures such as brain biopsies.

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“…LBP is a type of visual descriptor (or texture operator) used in computer vision for classification. 30 , 31 The LBP is calculated by thresholding intensity of circularly symmetric neighboring pixels with the intensity of the center pixel, in a local region of an image, as shown in Figure 2 . In this respect, it can be seen as a unifying approach to the typically diverse statistical and structural concepts of texture analysis.…”

Section: Feature Extractionmentioning

confidence: 99%

“…LBP is a type of visual descriptor (or texture operator) used in computer vision for classification 30,31 . The LBP is calculated by thresholding intensity of circularly symmetric neighboring pixels with the intensity of the center pixel, in a local region of an image, as shown in Figure 2.…”

Section: Feature Extractionmentioning

confidence: 99%

Covid‐19 detection from radiographs by feature‐reinforced ensemble learning

Elen

2022

Concurrency and Computation

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Summary The coronavirus (Covid‐19) epidemic continues to have a negative influence on the global population's well‐being and health. Scientists in many fields around the world are working non‐stop to find a solution to the prevention of this epidemic. In the field of computer science, this struggle is supported by studies on especially the analysis of X‐ray and CT images with artificial intelligence. In this study, two different ensemble learning models, including deep learning and a combination of machine learning methods, are presented for the detection of SARS‐CoV‐2 infection from X‐ray images. The main purpose of this study is to increase the classification ability of Residual Convolutional Neural Network (ResCNN), which is used as a deep learning method, with the assist of machine learning algorithms and extracted features from images. The proposed models were validated on a total of 5228 chest X‐ray images categorized as Normal, Pneumonia, and Covid‐19. The images in the dataset were sized in four different ways, 32 × 32, 64 × 64, 128 × 128, and 256 × 256, in order to analyze the validity of the proposed models in more detail. These four datasets were partitioned with the 10‐fold cross‐validation technique and converted into a total of 40 training and test data. Both proposed models use features derived from the ResCNN as the basis and test a certain number of machine learning algorithms with a majority voting technique by dividing them into subsets. In the architecture of the second model, it combines the features extracted from the Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) methods in addition to the features obtained from the ResCNN. It has been seen that the classification ability of both proposed models is better than the ResCNN in the experiments. In particular, the second model gives a similar classification score even though it is tested with images four‐times smaller (e.g., 32 × 32 vs. 128 × 128) than those used in the ResCNN. This shows that the model can give ideal results with lower computational cost.

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A comparative study for brain tumor detection in MRI images using texture features

Cited by 26 publications

References 34 publications

Breast cancer: Three‐class masses classification in mammograms using Apriori dynamic selection

Breast cancer: Three‐class masses classification in mammograms using Apriori dynamic selection

Feature Extraction from MRI ADC Images for Brain Tumor Classification Using Machine Learning Techniques

Covid‐19 detection from radiographs by feature‐reinforced ensemble learning

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