scholarly journals Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

2021 ◽  
Vol 9 (3) ◽  
pp. 150-156
Author(s):  
Hanimatim Mu'jizah ◽  
Dian Candra Rini Novitasari
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Image Data ◽  
Extraction Methods ◽  
Support Vector ◽  
Shape Feature ◽  
Histogram Of Oriented Gradient ◽  
Mammogram Image ◽  
Aided Diagnosis ◽  
Shape Feature Extraction

Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.

scholarly journals Shape Feature Extraction Methods of Rodents in the Field Based on Machine Vision

2019 ◽  
Vol 1345 ◽  
pp. 022040
Author(s):  
Deli Zhu ◽  
Bingqi Chen ◽  
Liliang Han ◽  
Yong Wang ◽  
Chaojie Wei ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Machine Vision ◽  
Extraction Methods ◽  
Shape Feature ◽  
Shape Feature Extraction

scholarly journals Pengenalan Karakter Tulisan Tangan Menggunakan Ekstraksi Fitur Bentuk Berbasis Chain Code

2020 ◽  
Vol 7 (4) ◽  
pp. 683
Author(s):  
Saniyatul Mawaddah ◽  
Nanik Suciati
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Character Recognition ◽  
Handwriting Recognition ◽  
Support Vector ◽  
Shape Feature ◽  
Performance Study ◽  
Chain Code ◽  
Chain Codes ◽  
Shape Feature Extraction

<p class="Abstrak">Pengenalan karakter tulisan tangan pada citra merupakan suatu permasalahan yang sulit untuk dipecahkan, dikarenakan terdapat perbedaan gaya penulisan pada setiap orang. Tahapan proses dalam pengenalan tulisan tangan diantaranya adalah <em>preprocessing</em>, ekstraksi fitur, dan klasifikasi. <em>Preprocessing</em> dilakukan untuk merubah citra tulisan tangan menjadi citra biner yang hanya mempunyai ketebalan 1 pixel melalui proses binerisasi dan <em>thining</em>. Kemudian pada tahap ekstraksi fitur, dipilih fitur bentuk karena fitur bentuk memiliki peran yang lebih penting dibanding 2 fitur visual lainnya (warna dan tekstur) pada pengenalan karakter tulisan tangan. Metode ekstraksi fitur bentuk yang dipilih dalam penelitian ini adalah metode berbasis <em>chain code</em> karena metode tersebut sering digunakan dalam beberapa penelitian pengenalan tulisan tangan. Pada penelitian ini, dilakukan studi kinerja dari ekstraksi fitur berbasis <em>chain code</em> pada pengenalan karakter tulisan tangan untuk mengetahui metode terbaiknya. Tiga metode ekstraksi fitur berbasis <em>chain code</em> yang digunakan dalam penelitian ini adalah <em>freeman chain code</em>, <em>differential chain code</em> dan <em>vertex chain code</em>. Setiap citra karakter diekstrak menggunakan 3 metode tersebut dengan tiga cara yaitu ekstraksi secara global, lokal 3x3, 5x5, dan 7x7. Setelah esktraksi fitur, dilakukan proses klasifikasi menggunakan support vector machine (SVM). Hasil eksperimen menunjukkan akurasi terbaik adalah pada model citra 7x7 dengan nilai akurasi <em>freeman chain code</em> sebesar 99.75%, <em>differential chain code</em> sebesar 99.75%, dan <em>vertex chain code</em> sebesar 98.6%.</p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The recognition of handwriting characters images is a difficult problems to be solved, because everyone has a different writing style. The step of handwriting recognition process are preprocessing, feature extraction, and classification. Preprocessing is done to convert handwritten images into binary images that only have 1 pixel thickness by using binarization and thinning. Then, in the feature extraction we select shape feature because it is more important than two other visual features (color and texture) in handwriting character recognition. Shape feature extraction method chosen in this research is chain code method because this method is often used in several studies for handwriting recognition. In this study, a performance study of feature extraction based on chain codes was carried out on handwriting character recognition to know the best chain code method. The three shape feature extraction based on chain code used in this study are freeman, differential and vertex chain codes. Each character image is extracted using these 3 methods in three ways: extraction globally, local 3x3, 5x5, and 7x7. After the extraction feature, the classification process is carried out using the support vector machine (SVM). The experimental results show that the best accuracy is in the 7x7 image model with the value of freeman chain code accuracy of 99.75%, the differential chain code of 99.75%, and the vertex chain code of 98.6%.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals Combination of Texture and Shape Features Using Machine and Deep Learning Algorithms for Breast Cancer Diagnosis

2021 ◽  
Author(s):  
Norhene Gargouri ◽  
Raouia Mokni ◽  
Alima Damak ◽  
Dorra Sellami ◽  
Riadh Abid
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Zernike Moments ◽  
Rotation Invariance ◽  
Invariance Property ◽  
Shape Feature ◽  
Shape Features ◽  
Shape Feature Extraction

Abstract Worldwide, breast cancer is a commonly occurring disease in women. Automatic diagnosis of the lesions based on mammographic images is playing an essential role to assist experts. A novel Computer-Aided Diagnosis (CADx) scheme of breast lesion classification is proposed in this paper based on an optimized combination of texture and shape features using machine and deep learning algorithms for mass classification as benign-malignant namely C(M-ZMs)*. The main advantage of using Zernike moments for shape feature extraction is their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our case. We implemented for texture feature extraction the Monogenic-Local Binary Pattern taking the advantage of lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. Therefore, we used Zernike moments for shape feature extraction due to their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our proposed system. The proposed system proves its performance on some challenging breast cancer cases where the lesions exist in dense breast tissues. Validation has been undertaken on 520 mammograms from the Digital Database for Screening Mammography Database (DDSM), yielding an accuracy rate of 99.5\%.

Breast Cancer Diagnosis System Based on Wavelet Analysis and Neural Networks

2014 ◽  
Vol 4 (1) ◽  
pp. 1-16 ◽  
Author(s):  
K. Taifi ◽  
S. Safi ◽  
M. Fakir ◽  
A. Elbalaoui
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Nearest Neighbor ◽  
Breast Cancer Diagnosis ◽  
Extraction Methods ◽  
Diagnosis System ◽  
Screening Programs ◽  
Mammographic Images ◽  
Aided Diagnosis

The high incidence of breast cancer has increased significantly in the recent years. The most familiar breast tumors types are mass and microcalcifications (Mcs). Mammogram is considered the most reliable method in early detection of breast cancer. Computer-aided diagnosis system can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. Several techniques can be used to accomplish this task. In this work, the authors present a preprocessing method, based on homomorphic filtering and wavelet, to extract the abnormal Mcs in mammographic images. The authors use four different methods of feature extraction for classification of normal and abnormal patterns in mammogram. Four different feature extraction methods are used here are Wavelet, Gist, Gabor and Tamura. A classification system based on neural network and nearest neighbor classification is used.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

Design of Text Categorization System Based on SVM

2012 ◽  
Vol 532-533 ◽  
pp. 1191-1195 ◽  
Author(s):  
Zhen Yan Liu ◽  
Wei Ping Wang ◽  
Yong Wang
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Extraction Methods ◽  
Support Vector ◽  
Text Representation ◽  
Text Feature ◽  
Categorization System ◽  
Classifier Training

This paper introduces the design of a text categorization system based on Support Vector Machine (SVM). It analyzes the high dimensional characteristic of text data, the reason why SVM is suitable for text categorization. According to system data flow this system is constructed. This system consists of three subsystems which are text representation, classifier training and text classification. The core of this system is the classifier training, but text representation directly influences the currency of classifier and the performance of the system. Text feature vector space can be built by different kinds of feature selection and feature extraction methods. No research can indicate which one is the best method, so many feature selection and feature extraction methods are all developed in this system. For a specific classification task every feature selection method and every feature extraction method will be tested, and then a set of the best methods will be adopted.

Headnote Prediction Using Machine Learning

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sarmad Mahar ◽  
Sahar Zafar ◽  
Kamran Nishat
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Active Learning ◽  
Text Classification ◽  
Extraction Methods ◽  
Text Summarization ◽  
Training Data ◽  
Second Step ◽  
Support Vector ◽  
Classification Algorithms

Headnotes are the precise explanation and summary of legal points in an issued judgment. Law journals hire experienced lawyers to write these headnotes. These headnotes help the reader quickly determine the issue discussed in the case. Headnotes comprise two parts. The first part comprises the topic discussed in the judgment, and the second part contains a summary of that judgment. In this thesis, we design, develop and evaluate headnote prediction using machine learning, without involving human involvement. We divided this task into a two steps process. In the first step, we predict law points used in the judgment by using text classification algorithms. The second step generates a summary of the judgment using text summarization techniques. To achieve this task, we created a Databank by extracting data from different law sources in Pakistan. We labelled training data generated based on Pakistan law websites. We tested different feature extraction methods on judiciary data to improve our system. Using these feature extraction methods, we developed a dictionary of terminology for ease of reference and utility. Our approach achieves 65% accuracy by using Linear Support Vector Classification with tri-gram and without stemmer. Using active learning our system can continuously improve the accuracy with the increased labelled examples provided by the users of the system.

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