scholarly journals Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features

2021 ◽  
Vol 11 ◽  
Author(s):  
Yan Hao ◽  
Shichang Qiao ◽  
Li Zhang ◽  
Ting Xu ◽  
Yanping Bai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Pathological Examination ◽  
Support Vector ◽  
Gray Level ◽  
Histopathological Images ◽  
Occurrence Matrix ◽  
Low Dimensional ◽  
Images Recognition

Breast cancer (BC) is the primary threat to women’s health, and early diagnosis of breast cancer is imperative. Although there are many ways to diagnose breast cancer, the gold standard is still pathological examination. In this paper, a low dimensional three-channel features based breast cancer histopathological images recognition method is proposed to achieve fast and accurate breast cancer benign and malignant recognition. Three-channel features of 10 descriptors were extracted, which are gray level co-occurrence matrix on one direction (GLCM1), gray level co-occurrence matrix on four directions (GLCM4), average pixel value of each channel (APVEC), Hu invariant moment (HIM), wavelet features, Tamura, completed local binary pattern (CLBP), local binary pattern (LBP), Gabor, histogram of oriented gradient (Hog), respectively. Then support vector machine (SVM) was used to assess their performance. Experiments on BreaKHis dataset show that GLCM1, GLCM4 and APVEC achieved the recognition accuracy of 90.2%-94.97% at the image level and 89.18%-94.24% at the patient level, which is better than many state-of-the-art methods, including many deep learning frameworks. The experimental results show that the breast cancer recognition based on high dimensional features will increase the recognition time, but the recognition accuracy is not greatly improved. Three-channel features will enhance the recognizability of the image, so as to achieve higher recognition accuracy than gray-level features.

scholarly journals LBP and GLCM Based Image Forgery Recognition

2018 ◽  
Vol 7 (4.6) ◽  
pp. 217
Author(s):  
D. Vaishnavi ◽  
T. S. Subashini ◽  
G. N. Balaji ◽  
D. Mahalakshmi
Keyword(s):  
Neural Network ◽  
Local Binary Pattern ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Gray Level ◽  
Image Splicing ◽  
Naked Eye ◽  
Occurrence Matrix

The forgery of digital images became very easy and it’s very difficult to ascertain the authenticity of such images by naked eye. Among the various kinds of image forgeries, image splicing is a frequent and widely used technique. Even though various methods are available to detect image splicing forgery, authors have attempted to provide a novel hybrid method which can yield greater accuracy, sensitivity and specificity. In this method, gray level co-occurrence matrix (GLCM) features are extracted using local binary pattern (LBP) operator on the image and the detection of the splicing forged images among the authentic images is done using the popular pattern recognition algorithms such as combined k-NN (Comb-KNN), back propagation neural network (BPNN) and support vector machine (SVM). The recorded results are also compared with the existing results of the previous studies to ascertain the quality of the results.  

scholarly journals PERBAIKAN KONTRAS CITRA MAMMOGRAM PADA KLASIFIKASI KANKER PAYUDARA BERDASARKAN FITUR GRAY-LEVEL CO-OCCURRENCE MATRIX

2020 ◽  
Vol 3 (1) ◽  
pp. 46-51
Author(s):  
Febri Liantoni ◽  
Agus Santoso
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Median Filter ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Support Vector ◽  
Gray Level ◽  
Image Construction ◽  
Occurrence Matrix ◽  
Mammogram Images

In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram equalization are used to improve image quality. Feature extraction using Gray-Level Co-Occurrence Matrix (GLCM) with 5 features, entropy, correlation, contrast, homogeneity, and variance. The final step is the classification using Radial Basis Function Neural Network (RBFNN) and Support Vector Machine (SVM). Based on the hypotheses that have been tested and discussed, the accuracy for RBFNN is 86.27%, while the accuracy for SVM is 84.31%. This shows that the RBFNN method is better than SVM in distinguishing types of breast cancer. These results prove the process of improving image construction using histogram equalization and the median filter is useful in the classification process.

KLASIFIKASI MASSA PADA CITRA MAMMOGRAM MENGGUNAKAN KOMBINASI SELEKSI FITUR F-SCORE DAN LS-SVM

Teknologi ◽  
2016 ◽  
Vol 6 (1) ◽  
pp. 27
Author(s):  
Muhammad I. Rosadi ◽  
Agus Z. Arifin ◽  
Anny Yuniarti
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Feature Selection ◽  
Support Vector ◽  
Gray Level ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Occurrence Matrix ◽  
Kernel Parameters

ABSTRAKKanker payudara adalah penyakit yang paling umum diderita oleh perempuan pada banyak negara. Pemeriksaan kanker payudara dapat dilakukan menggunakan citra Mammogram dengan teknologi sistem Computer-Aided Detection (CAD). Analisis CAD yang telah dikembangkan adalah ekstraksi fitur GLCM, reduksi/seleksi fitur, dan SVM. Pada SVM (Support Vector Machine) maupun LS-SVM (Least Square Support Vector Machine) terdapat tiga masalah yang muncul, yaitu: Bagaimana memilih fungsi kernel, berapa jumlah fitur input yang dioptimalkan, dan bagaimana menentukan parameter kernel terbaik. Jumlah fitur dan nilai parameter kernel yang diperlukan saling mempengaruhi, sehingga seleksi fitur diperlukan dalam membangun sistem klasifikasi. Pada penelitian ini bertujuan untuk mengklasifikasi massa pada citra Mammogram berdasarkan dua kelas yaitu kelas kanker jinak dan kelas kanker ganas. Ekstraksi fitur menggunakan Gray Level Co-occurrence Matrix (GLCM). Hasil proses ekstraksi fitur tersebut kemudian diseleksi mengunakan metode F-Score. F-Score diperoleh dengan menghitung nilai diskriminan data hasil ekstraksi fitur di antara data dua kelas pada data training. Nilai F-Score masing-masing fitur kemudian diurutkan secara descending. Hasil pengurutan tersebut digunakan untuk membuat kombinasi fitur. Kombinasi fitur tersebut digunakan sebagai input LS-SVM. Dari hasil uji coba penelitian ini didapatkan, bahwa menggunakan kombinasi seleksi fitur sangat berpengaruh terhadap tingkat akurasi. Akurasi terbaik didapat dengan menggunakan LS-SVM RBF dan SVM RBF baik dengan kombinasi seleksi fitur, maupun tanpa kombinasi seleksi fitur dengan nilai akurasi yaitu 97,5%. Selain itu juga seleksi fitur mampu mengurangi waktu komputasi.Kata Kunci: F-Score, GLCM, kanker payudara, LS-SVM.ABSTRACTBreast cancer is the most common disease suffered by women in many countries. Breast cancer screening can be done using a mammogram image. Computer-aided detection system (CAD). CAD analysis that has been developed is GLCM efficient feature extraction, reduction / feature selection and SVM. In SVM (Support Vector Machine) and LS-SVM (Support Vector Machine Square least) there are three problems that arise, namely; how to choose the kernel function, how many input fea-tures are optimal, and how to determine the best kernel parameters. The number of fea-tures and value required kernel parameters affect each other, so that the selection of the features needed to build a system of classification. In this study aims to classify image of masses on digital mammography based on two classes benign cancer and malignant cancer. Feature extraction using gray level co-occurrence matrix (GLCM). The results of the feature extraction process then selected using the method F-Score. F-Score is obtained by calculating the value of the discriminant feature extraction results data between two classes of data in the data training. Value F-Score of each feature and then sorted in descending order. The sequenc-ing results are used to make the combination of fea-tures. The combination of these features are used as input LS-SVM. From the experiments that use a combination of feature selection affects the accuracy ting-kat. Best accuracy obtained using LS-SVM and SVM RBF RBF with combi-nation or without the combination of feature selection with accuracy value is 97.5%. It also features a selection able to curate the computa-tion time.Keywords: Breast Cancer, F-Score, GLCM, LS-SVM.

Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection

2020 ◽  
Vol 9 (2) ◽  
pp. 25-44
Author(s):  
Usha N. ◽  
Sriraam N. ◽  
Kavya N. ◽  
Bharathi Hiremath ◽  
Anupama K Pujar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Classification Accuracy ◽  
Breast Imaging ◽  
Texture Features ◽  
Support Vector ◽  
Risk Of Death ◽  
Automated Method ◽  
Occurrence Matrix ◽  
Aided Diagnosis ◽  
Mammogram Images

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.

An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach

2020 ◽  
pp. 095440622094226 ◽  
Author(s):  
Amanda Campos Souza ◽  
Gulliver Catão Silva ◽  
Lecino Caldeira ◽  
Fernando Marques de Almeida Nogueira ◽  
Moisés Luiz Lagares Junior ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Intelligence ◽  
Low Carbon Steel ◽  
Support Vector ◽  
Gray Level ◽  
Low Carbon ◽  
Identification Rate ◽  
Artificial Neural ◽  
Occurrence Matrix

This work focuses on the identification of five of the most common ferritic morphologies present in welded fusion zones of low carbon steel through images acquired by photomicrographies. With this regards, we discuss the importance of the gray-level co-occurrence matrix to extract the features to be used as the input of the computational intelligence techniques. We use artificial neural networks and support vector machines to identify the proportions of each morphology and present the error identification rate for each technique. The results show that the use of gray-level co-occurrence extraction allows a less intense computational model with statistical validity and the support vector machine as a computational intelligence technique allows smaller variability when compared to the artificial neural networks.

scholarly journals Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Abdullah-Al Nahid ◽  
Mohamad Ali Mehrabi ◽  
Yinan Kong
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Classification ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Statistical Information ◽  
Support Vector ◽  
Biomedical Image ◽  
Histopathological Images ◽  
Photography Analysis

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F-Measure value is achieved on both the 40x and 100x datasets.

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