Classification of Microcalcifications in Mammograms using 2D Discrete Wavelet Transform and Random Forest

Fadil, Rabie; Jackson, Andie; Majd, Badr Abou El; Ghazi, Hassan El; Kaabouch, Naima

doi:10.1109/eit48999.2020.9208290

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…This research presented an automated and computer-based approach to classify breast microcalcifications on mammography images using discrete wavelet transform-random forest (DWT-RF). The results show an accuracy of 95%, sensitivity of 93%, and specificity of 97% 16) .…”

Section: Introductionmentioning

confidence: 90%

Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

Sumarti,

Sheilla Rully Anggita,

Hartono

et al. 2023

Evergreen

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Breast cancer is the most common cancer in Indonesia. One way to detect it early is by screening using mammography. Previous trials showed that mammography screening in women aged 40-49 years could reduce breast cancer mortality by 25%. However, misdiagnosis may occur abaout breast density and the patient's physical size due to machines. In addition, human reader errors can occur concerning the reader's experience and perception. Therefore, diagnostic aids are needed to distinguish benign and malignant cases and receive appropriate treatment. The methodology in this research consists of three stages: preprocessing, texture feature extraction, and data classification. Preprocessing consists of filtering, contrast, cropping, and resizing, while texture feature extraction consist of Histogram and (Gray Level Co-occurrence Matrix) GLCM. Data classification using Support Vector Machines (SVM), Naive Bayes, Multi-Layer Perceptron (MLP), Multiclass classifier, and Random Forest methods with Weka Machine Learning software. It produces an accuracy of 62.00%, 62.00%, 88.00%, 82.00%, and 100.00%, respectively. The results of data classification using the Random Forest method show that the accuracy, specifications, and specificity reach 100%. Random forest can be used as the most optimal classification method to distinguish benign and malignant cases based on texture features in mammography images using Weka Machine Learning software. This can help radiologists and medical professionals to diagnose cases and take further steps, such as therapy.

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Section: Introductionmentioning

confidence: 90%

Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

Sumarti,

Sheilla Rully Anggita,

Hartono

et al. 2023

Evergreen

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“…Random forest performance was compared to other suggested approaches, and it was determined that random forest performed well. Fadil et al [ 55 ] developed a computer-based automated approach for detecting and segmenting the MC regions in mammogram images using the discrete wavelet transform and random forest. The results suggest that RF achieved an accuracy of 0.83 and a sensitivity of 0.78.…”

Section: Methodsmentioning

confidence: 99%

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

Mahmood

Pei

et al. 2021

Cancers

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Microcalcifications in breast tissue can be an early sign of breast cancer, and play a crucial role in breast cancer screening. This study proposes a radiomics approach based on advanced machine learning algorithms for diagnosing pathological microcalcifications in mammogram images and provides radiologists with a valuable decision support system (in regard to diagnosing patients). An adaptive enhancement method based on the contourlet transform is proposed to enhance microcalcifications and effectively suppress background and noise. Textural and statistical features are extracted from each wavelet layer’s high-frequency coefficients to detect microcalcification regions. The top-hat morphological operator and wavelet transform segment microcalcifications, implying their exact locations. Finally, the proposed radiomic fusion algorithm is employed to classify the selected features into benign and malignant. The proposed model’s diagnostic performance was evaluated on the MIAS dataset and compared with traditional machine learning models, such as the support vector machine, K-nearest neighbor, and random forest, using different evaluation parameters. Our proposed approach outperformed existing models in diagnosing microcalcification by achieving an 0.90 area under the curve, 0.98 sensitivity, and 0.98 accuracy. The experimental findings concur with expert observations, indicating that the proposed approach is most effective and practical for early diagnosing breast microcalcifications, substantially improving the work efficiency of physicians.

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“…Various features have been studied in the literature to classify benign and malignant microcalcifications. Research by Fadil et al [15] used 2D discrete wavelet transform for contrast enhancement of the microcalcifications and extracted eight textural features on the GLCM, achieving 95% accuracy and 0.92 AUC using the random forest (RF) classifier. On the other hand, Suhail et al [46] present a way to obtain a single feature value by applying a scalable linear fisher discriminant analysis (LDA) approach, achieving up to 96% accuracy with 0.95 AUC using SVM.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Such features included intensity, statistical, shape, and textural features [5]. The grey-level cooccurrence matrix (GLCM), which calculates the occurrence of various grey levels in a region of interest (ROI), is a well-known texture feature and is utilised extensively in the literature [5,[15][16][17]. Nevertheless, all of these features focus on local information of the images and are often burdened with details, resulting in data complexity [18].…”

Section: Introductionmentioning

confidence: 99%

Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection

Malek

Alias

Razak

et al. 2023

Cancers

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Microcalcifications in mammogram images are primary indicators for detecting the early stages of breast cancer. However, dense tissues and noise in the images make it challenging to classify the microcalcifications. Currently, preprocessing procedures such as noise removal techniques are applied directly on the images, which may produce a blurry effect and loss of image details. Further, most of the features used in classification models focus on local information of the images and are often burdened with details, resulting in data complexity. This research proposed a filtering and feature extraction technique using persistent homology (PH), a powerful mathematical tool used to study the structure of complex datasets and patterns. The filtering process is not performed directly on the image matrix but through the diagrams arising from PH. These diagrams will enable us to distinguish prominent characteristics of the image from noise. The filtered diagrams are then vectorised using PH features. Supervised machine learning models are trained on the MIAS and DDSM datasets to evaluate the extracted features’ efficacy in discriminating between benign and malignant classes and to obtain the optimal filtering level. This study reveals that appropriate PH filtering levels and features can improve classification accuracy in early cancer detection.

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Classification of Microcalcifications in Mammograms using 2D Discrete Wavelet Transform and Random Forest

Cited by 9 publications

References 24 publications

Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

Texture-Based Classification of Benign and Malignant Mammography Images using Weka Machine Learning: An Optimal Approach

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach

Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection

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