Computer-aided classification of rheumatoid arthritis in finger joints using frequency domain optical tomography

2009 ◽  
Author(s):  
C. D. Klose ◽  
H. K. Kim ◽  
U. Netz ◽  
S. Blaschke ◽  
P. A. Zwaka ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Frequency Domain ◽  
Optical Tomography ◽  
Finger Joints ◽  
Computer Aided

scholarly journals Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification

2013 ◽  
Vol 18 (7) ◽  
pp. 076002 ◽  
Author(s):  
Ludguier D. Montejo ◽  
Jingfei Jia ◽  
Hyun K. Kim ◽  
Uwe J. Netz ◽  
Sabine Blaschke ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Image Classification ◽  
Optical Tomography ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided classification of optical images for diagnosis of osteoarthritis in the finger joints

2011 ◽  
Vol 19 (4) ◽  
pp. 531-544 ◽  
Author(s):  
Jiang Zhang ◽  
James Z. Wang ◽  
Zhen Yuan ◽  
Eric S. Sobel ◽  
Huabei Jiang
Keyword(s):  
Finger Joints ◽  
Optical Images ◽  
Computer Aided

scholarly journals Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction

2013 ◽  
Vol 18 (7) ◽  
pp. 076001 ◽  
Author(s):  
Ludguier D. Montejo ◽  
Jingfei Jia ◽  
Hyun K. Kim ◽  
Uwe J. Netz ◽  
Sabine Blaschke ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Feature Extraction ◽  
Optical Tomography ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

Imaging of Rheumatoid Arthritis in Finger Joints by Sagittal Optical Tomography

2001 ◽  
Vol 16 (4) ◽  
pp. 306-310 ◽  
Author(s):  
Uwe Netz ◽  
Jürgen Beuthan ◽  
Hans-Joachim Cappius ◽  
Hans-Christian Koch ◽  
Alexander D. Klose ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Optical Tomography ◽  
Finger Joints

Saggital optical tomography for the diagnosis of rheumatoid arthritis in finger joints

2002 ◽  
Author(s):  
Andreas H. Hielscher ◽  
Alexander D. Klose ◽  
Uwe Netz ◽  
Hans-Joachim Cappius ◽  
Jürgen Beuthan
Keyword(s):  
Rheumatoid Arthritis ◽  
Optical Tomography ◽  
Finger Joints

Two- and three-dimensional optical tomography of finger joints for diagnostics of rheumatoid arthritis

1998 ◽  
Author(s):  
Alexander D. Klose ◽  
Andreas H. Hielscher ◽  
Kenneth M. Hanson ◽  
Juergen Beuthan
Keyword(s):  
Rheumatoid Arthritis ◽  
Optical Tomography ◽  
Three Dimensional ◽  
Finger Joints

Development and Evaluation of Fuzzy Criteria for the Diagnosis of Rheumatoid Arthritis

1996 ◽  
Vol 35 (04/05) ◽  
pp. 334-342 ◽  
Author(s):  
K.-P. Adlassnig ◽  
G. Kolarz ◽  
H. Leitich
Keyword(s):  
Rheumatoid Arthritis ◽  
Reference Model ◽  
American Rheumatism Association ◽  
Uniform Definition ◽  
Sensitivity Rate ◽  
Fuzzy Techniques ◽  
Definition Of ◽  
Different Levels ◽  
Specificity Rate

Abstract:In 1987, the American Rheumatism Association issued a set of criteria for the classification of rheumatoid arthritis (RA) to provide a uniform definition of RA patients. Fuzzy set theory and fuzzy logic were used to transform this set of criteria into a diagnostic tool that offers diagnoses at different levels of confidence: a definite level, which was consistent with the original criteria definition, as well as several possible and superdefinite levels. Two fuzzy models and a reference model which provided results at a definite level only were applied to 292 clinical cases from a hospital for rheumatic diseases. At the definite level, all models yielded a sensitivity rate of 72.6% and a specificity rate of 87.0%. Sensitivity and specificity rates at the possible levels ranged from 73.3% to 85.6% and from 83.6% to 87.0%. At the superdefinite levels, sensitivity rates ranged from 39.0% to 63.7% and specificity rates from 90.4% to 95.2%. Fuzzy techniques were helpful to add flexibility to preexisting diagnostic criteria in order to obtain diagnoses at the desired level of confidence.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

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