Three-class classification in computer-aided diagnosis of breast cancer by support vector machine

2004 ◽  
Author(s):  
Xuejun Sun ◽  
Wei Qian ◽  
Dansheng Song
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Classification of Thyroid Nodules in Ultrasound Images Using Direction-Independent Features Extracted by Two-Threshold Binary Decomposition

2019 ◽  
Vol 18 ◽  
pp. 153303381983074 ◽  
Author(s):  
Antonin Prochazka ◽  
Sumeet Gulati ◽  
Stepan Holinka ◽  
Daniel Smutek
Keyword(s):  
Support Vector Machine ◽  
Ultrasound Imaging ◽  
Random Forests ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Computer Aided ◽  
Binary Decomposition ◽  
Independent Features ◽  
Aided Diagnosis

In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.

scholarly journals Computer–aided diagnosis of diabetes using least square support vector machine

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
Behnaz Naghash Almasi ◽  
Omid Naghash Almasi ◽  
Mina Kavousi ◽  
Amirhossein Sharifinia
Keyword(s):  
Support Vector Machine ◽  
Computer Aided Diagnosis ◽  
Least Square ◽  
Support Vector ◽  
Computer Aided ◽  
Aided Diagnosis

KLASIFIKASI TUMOR JINAK DAN TUMOR GANAS PADA CITRA MAMMOGRAM MENGGUNAKAN GRAY LEVEL CO-OCCURRENCE MATRIX (GLCM) DAN SUPPORT VECTOR MACHINE (SVM)

2021 ◽  
Vol 26 (2) ◽  
pp. 176-186
Author(s):  
Lulu Mawaddah Wisudawati
Keyword(s):  
Support Vector Machine ◽  
World Health Organization ◽  
Computer Aided Diagnosis ◽  
World Health ◽  
Support Vector ◽  
Gray Level ◽  
Computer Aided ◽  
Health Organization ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Kanker payudara merupakan penyebab utama kematian pada wanita. Data Global Cancer Observatory 2018 dari World Health Organization (WHO, 2020) menunjukkan kasus kanker yang paling banyak terjadi di Indonesia adalah kanker payudara, yakni 58.256 kasus atau 16.7% dari total 348.809 kasus kanker. Mamografi merupakan teknik yang paling umum digunakan dalam mendeteksi tumor payudara menggunakan sistem sinar-X dosis rendah. Ada beberapa tipe abnormalitas dalam citra mammogram, yaitu mikrokalsifikasi dan massa. Penelitian ini bertujuan untuk meningkatkan performa sistem Computer-Aided Diagnosis (CAD) dalam mengklasifikasi tumor jinak dan tumor ganas dengan mengembangkan metode ekstraksi fitur menggunakan Gray Level Co-Occurrence Matrix (GLCM) dan metode klasifikasi menggunakan Support Vector Machine (SVM). Uji coba dilakukan dengan menggunakan database DDSM dengan 256 citra abnormal (95 tumor jinak dan 161 tumor ganas) menghasilkan nilai akurasi sebesar 83.59% dengan nilai sensitivitas dan spesifisitas 87.58% dan 76.84%. Selain itu, didapatkan nilai AUC sebesar 0.98%. Metode tersebut menunjukkan bahwa sistem memberikan hasil performa yang baik dalam mengklasifikasi tumor jinak dan tumor ganas.

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