Breast Cancer Classification as Malignant or Benign Based on Texture Features Using Multilayer Perceptron

Salman, Nassir H.; Ali, Semaa; Kailan, Safa L.; Mohammed, Faisel G.

doi:10.5013/ijssst.a.20.01.12

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Then the output of the preprocessing phase is used for the segmentation phase, which is done using the RG technique. [20]. the authors have proposed a system for detect potential cancer tumors in mammograms, the detection is made through automatically dividing breast images by combining a hybrid density slicing technique with the adaptive k-means algorithm, also by dividing breast images and extracting areas of cancer, then calculating their properties using the method of the region growing.…”

Section: Related Workmentioning

confidence: 99%

Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

AlSudani

Hussain

Khalil³

2020

MJS

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Cancer of the breast is one of the world's most prevalent causes of death for women. Early and efficient identification is important for can care choices and reducing mortality. Mammography is the most effective early breast cancer detection process. Radiologists cannot however make a detailed and reliable assessment of mammograms due to fatigue or poor image quality. The main aim of this work is to establish a new approach to help radiologists identify anomalies and improve diagnostic precision. The proposed method has been applied through the implementation of preprocessing then segmentation of the images to get the region of interest that was used to find a texture features that were calculated based on first Order (statistical features), Gray-Level Co-Occurrence Matrix (GLCM), and Local Binary Patterns LBP (LBP). In the features selection phase mutual information (MI) algorithm is applied to choose from the extracted features collection suitable features. Finally, Multilayer Perceptron has been applied in two stages to classify the mammography images first to normal or abnormal, and secondly, classification of abnormal images into benign or malignant images. This method was implemented and gave an accuracy of 92.91 % for the first level and 93.15% for the second level classification.

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Section: Related Workmentioning

confidence: 99%

Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

AlSudani

Hussain

Khalil³

2020

MJS

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“…In 2019 [26] Salman, Nassir, and Semaa Ibrahim, the authors have proposed a system for detect potential cancer tumors in mammograms, the detection is made through automatically dividing breast images by combining hybrid density slicing technique with the adaptive k-means algorithm, also by dividing breast images and extracting areas of cancer. (GLCM) have been used with proposed features that are gray level density matrices (GLDM) to detect abnormal tissue using MLP classifiers.…”

Section: Gray Level Co-occurrences Matrix (Glcm) Featuresmentioning

confidence: 99%

Classification of mammograms based on features extraction techniques using support vector machine

Saeed

Saleh

Khalel³

2020

Comput. Sci. Inf. Technol.

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Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the pre-processing stage. Secondly, in the segmentation phase, a Hybrid Bounding Box and Region Growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the ROI. In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM), Finally, SVM has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the Mammogram image analysis Society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.

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“…Several studies have developed image processing techniques and machine learning techniques to classify and detect breast cancer, such as CAD using mammograms. The development of a CAD system by several researchers to detect and diagnose breast cancer by identifying salient features of mammogram images, such as color features [7], texture [8], [9], statistics, and shape [10], [11], have also been proposed near-field imaging system using ultra-wideband [12]. Yong [13] proposed Deep Learning models namely DenseNet-169 and EfficientNet-B5 that automatically detects breast cancer according to the breast density.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Feature Selection on Gray Level Co-Occurrence Matrix (GLCM) for the Four Breast Cancer Classifications

Mawarni

Utaminingrum

Mahmudy

2022

JBBBE

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Breast cancer is ranked first as the most common cancer case affecting women in the world. Early detection of breast cancer can increase the chances of survival in patients. The role of the radiologist is necessary for the detection of breast cancer, and the radiologists often have limitations in conducting disease consultations with so many patients. The detection gives a subjective result because the process is based on the decision-making of the radiologists. In this work, we proposed a system to detect and classify breast cancer accurately to anticipate delays in patient handling and subjective result. We proposed a digital image processing method using mammograms to classify breast cancer into four categories based on tissue density, namely BI-RADS I, II, III, and IV. The main stages carried out in this research are images processing, feature extraction, data normalization, feature selection, classification, and parameter optimization. This method uses GLCM to extract texture features and two feature selection methods namely, RFE-RF and Chi-Square. The method was tested with various classifiers such as SVM, KNN, Random Forests, and Decision Trees. The hyper-parameters of the classifier were optimized using GridSearch. The final result is measure using accuracy. In this work, Random Forest with the RFE-RF gives the highest accuracy of 99.7%. Feature selection offers a significant impact on improving accuracy. The results of this work prove that our system can classify breast cancer with high accuracy. So that our system can solve problems to assist radiologists in screening mammograms and help make decisions to diagnose patients with breast cancer based on density.

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Breast Cancer Classification as Malignant or Benign Based on Texture Features Using Multilayer Perceptron

Cited by 4 publications

References 6 publications

Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

Classification of mammograms based on features extraction techniques using support vector machine

The Effect of Feature Selection on Gray Level Co-Occurrence Matrix (GLCM) for the Four Breast Cancer Classifications

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