scholarly journals Computer-Aided Detection of Incidental Lumbar Spine Fractures from Routine Dual-Energy X-Ray Absorptiometry (DEXA) Studies Using a Support Vector Machine (SVM) Classifier

2019 ◽  
Vol 33 (1) ◽  
pp. 204-210 ◽  
Author(s):  
Samir D. Mehta ◽  
Ronnie Sebro
Keyword(s):  
Support Vector Machine ◽  
Lumbar Spine ◽  
Dual Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Spine Fractures ◽  
Computer Aided Detection ◽  
X Ray ◽  
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.

scholarly journals Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier

2021 ◽  
Vol 11 (4) ◽  
pp. 7296-7301
Author(s):  
H. A. Owida ◽  
A. Al-Ghraibah ◽  
M. Altayeb
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Mel Frequency Cepstral Coefficients ◽  
X Ray ◽  
Proposed Model ◽  
Public Data ◽  
Chest X Ray

The shortage and availability limitation of RT-PCR test kits and is a major concern regarding the COVID-19 pandemic. The authorities' intention is to establish steps to control the propagation of the pandemic. However, COVID-19 is radiologically diagnosable using x-ray lung images. Deep learning methods have achieved cutting-edge performance in medical diagnosis software assistance. In this work, a new diagnostic method for detecting COVID-19 disease is implemented using advanced deep learning. Effective features were extracted using wavelet analysis and Mel Frequency Cepstral Coefficients (MFCC) method, and they used in the classification process using the Support Vector Machine (SVM) classifier. A total of 2400 X-ray images, 1200 of them classified as Normal (healthy) and 1200 as COVID-19, have been derived from a combination of public data sets to verify the validity of the proposed model. The experimental results obtained an overall accuracy of 98.8% by using five wavelet features, where the classification using MFCC features, MFCC-delta, and MFCC-delta-delta features reached accuracy around 97% on average. The results show that the proposed model has reached the required level of success to be applicable in COVID 19 diagnosis.

Mammogram Classification Using Support Vector Machine

2017 ◽  
pp. 587-614
Author(s):  
Youssef Ben Youssef ◽  
Elhassane Abdelmounim ◽  
Abdelaziz Belaguid
Keyword(s):  
Support Vector Machine ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Local Descriptors ◽  
Mammographic Image ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.

Mammogram Classification Using Support Vector Machine

2020 ◽  
pp. 894-921
Author(s):  
Youssef Ben Youssef ◽  
Elhassane Abdelmounim ◽  
Abdelaziz Belaguid
Keyword(s):  
Support Vector Machine ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Svm Classifier ◽  
Local Descriptors ◽  
Mammographic Image ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.

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