Combination of block difference inverse probability features and support vector machine to reduce false positives in computer-aided detection for massive lesions in mammographic images

2013 ◽  
Author(s):  
V. D. Nguyen ◽  
D. T. Nguyen ◽  
T. D. Nguyen ◽  
Q. D. Truong ◽  
M. D. Le
Keyword(s):  
Support Vector Machine ◽  
False Positives ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Inverse Probability ◽  
Mammographic Images ◽  
Computer Aided

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.

Computer-Aided Detection of Polyps in CT Colonography by Means of Feature Selection and Massive-Training Support Vector Regression

2012 ◽  
pp. 178-201
Author(s):  
Jian-Wu Xu ◽  
Kenji Suzuki
Keyword(s):  
Feature Selection ◽  
Support Vector Regression ◽  
Ct Colonography ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Training Time ◽  
Computer Aided ◽  
Wilks Lambda

One of the major challenges in current Computer-Aided Detection (CADe) of polyps in CT Colonography (CTC) is to improve the specificity without sacrificing the sensitivity. If a large number of False Positive (FP) detections of polyps are produced by the scheme, radiologists might lose their confidence in the use of CADe. In this chapter, the authors used a nonlinear regression model operating on image voxels and a nonlinear classification model with extracted image features based on Support Vector Machines (SVMs). They investigated the feasibility of a Support Vector Regression (SVR) in the massive-training framework, and the authors developed a Massive-Training SVR (MTSVR) in order to reduce the long training time associated with the Massive-Training Artificial Neural Network (MTANN) for reduction of FPs in CADe of polyps in CTC. In addition, the authors proposed a feature selection method directly coupled with an SVM classifier to maximize the CADe system performance. They compared the proposed feature selection method with the conventional stepwise feature selection based on Wilks’ lambda with a linear discriminant analysis classifier. The FP reduction system based on the proposed feature selection method was able to achieve a 96.0% by-polyp sensitivity with an FP rate of 4.1 per patient. The performance is better than that of the stepwise feature selection based on Wilks’ lambda (which yielded the same sensitivity with 18.0 FPs/patient). To test the performance of the proposed MTSVR, the authors compared it with the original MTANN in the distinction between actual polyps and various types of FPs in terms of the training time reduction and FP reduction performance. The authors’ CTC database consisted of 240 CTC datasets obtained from 120 patients in the supine and prone positions. With MTSVR, they reduced the training time by a factor of 190, while achieving a performance (by-polyp sensitivity of 94.7% with 2.5 FPs/patient) comparable to that of the original MTANN (which has the same sensitivity with 2.6 FPs/patient).

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