Classification of Mammogram Masses Using GLCM on LBP and Non-overlapping Blocks of Varying Sizes

Kurdi, Heba; Alkubeyyer, Atheer; Alabdullatif, Reema; Althnian, Alhanoof

doi:10.1007/978-981-16-5348-3_20

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Conventional (standard) LBP was extended later by adapting uniform patterns to have more discriminative features 27 . When the calculated code has two 0–1 or 1–0 transitions at max, then it is considered uniform 28 . In this study, LBP features were obtained by using a uniform rotation‐invariant LBP operator.…”

Section: Methodsmentioning

confidence: 99%

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BUS‐CAD: A computer‐aided diagnosis system for breast tumor classification in ultrasound images using grid‐search‐optimized machine learning algorithms with extended and Boruta‐selected features

Özcan

2023

Int J Imaging Syst Tech

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Breast cancer has become the most prominent type of cancer in the world. Early detection of breast cancer plays an important role in optimal treatment planning to decrease mortality. Breast ultrasound is widely used in diagnosing breast masses. Applications of machine learning in ultrasound imaging‐based classification have shown promising potential for early and accurate detection of breast cancer. In this study, a new computer‐aided diagnosis system based on machine learning techniques for breast cancer classification is proposed. Feature space is extended by using hybrid feature representations that combine both global and local texture statistics. A two‐step feature selection process is implemented using Boruta all‐relevant feature selection algorithm and iterative correlation analysis. A grid‐search strategy is followed along with 20 times repeated 10‐fold randomly stratified cross‐validation to optimize machine learning algorithms. Fourteen classification models based on random forest (RF) and support vector machine trained using all combinations of global features and the features driven from gray‐level co‐occurrence matrix and local binary patterns are tested. The experiments showed that the RF classifier on the hybrid feature vector that combines all global and local features achieved the best classification performance with average accuracy and area under the curve of 97.81% and 99.80%, respectively. The results suggest that the proposed system efficiently improves the classification performance of breast lesions on ultrasound images and can assist clinical decision‐making.

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Section: Methodsmentioning

confidence: 99%

“…27 When the calculated code has two 0-1 or 1-0 transitions at max, then it is considered uniform. 28 In this study, LBP features were obtained by using a uniform rotation-invariant LBP operator. The procedure was implemented into the proposed pipeline using the MATLAB "extractLBPFeatures" function.…”

Section: Feature Extractionmentioning

confidence: 99%

BUS‐CAD: A computer‐aided diagnosis system for breast tumor classification in ultrasound images using grid‐search‐optimized machine learning algorithms with extended and Boruta‐selected features

Özcan

2023

Int J Imaging Syst Tech

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Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic images

Yousefi,

Najjar-Ghabel,

Danehchin

et al. 2024

Journal of King Saud University - Computer and Information Scie

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Toward Effective Breast Cancer Detection in Thermal Images Using Efficient Feature Selection Algorithm and Feature Extraction Methods

Moayedi,

Rezai,

Hamidpour

2024

Biomed. Eng. Appl. Basis Commun.

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In this paper, an intelligent method is developed for improving the performance of the Computer-Aided Detection (CAD) system. The research objective is to improve the performance of the CAD system in Breast Cancer (BC) detection with high accuracy using thermal images. The research strategy is efficient using feature extraction, feature selection, classification and artificial intelligence methods. In the developed method, the features in the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are extracted from images. The features are selected using the firefly feature selection algorithm. These selected features are observed to be relevant for the abnormality detection in healthy and unhealthy breasts. The [Formula: see text]-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision-Tree (D-Tree) classifiers are then applied to these features for the detection of malignancy in the breast. The breast thermograms of 200 subjects available at the Database for Mastology Research for breast research using InfraRed images, DMR-IR database, are considered for evaluation of our intelligent method. The results demonstrate that the accuracy is 98.8%, 81.5%, and 95%, the sensitivity is 99%, 83.15%, and 95.91%, and the specificity is 98.2%, 80%, and 94.11% when using SVM, kNN, and D-Tree classifier algorithm, respectively. This reveals the effectiveness of our intelligent method to improve the accuracy of the CAD system in the BC detection.

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Classification of Mammogram Masses Using GLCM on LBP and Non-overlapping Blocks of Varying Sizes

Cited by 3 publications

References 18 publications

BUS‐CAD: A computer‐aided diagnosis system for breast tumor classification in ultrasound images using grid‐search‐optimized machine learning algorithms with extended and Boruta‐selected features

BUS‐CAD: A computer‐aided diagnosis system for breast tumor classification in ultrasound images using grid‐search‐optimized machine learning algorithms with extended and Boruta‐selected features

Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic images

Toward Effective Breast Cancer Detection in Thermal Images Using Efficient Feature Selection Algorithm and Feature Extraction Methods

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