The Measurement of Performance in Probabilistic Diagnosis

1978 ◽  
Vol 17 (04) ◽  
pp. 227-237 ◽  
Author(s):  
J. Hilden ◽  
J. D. F. Habbema ◽  
B. Bjerregaard
Keyword(s):  
Acute Abdomen ◽  
External Validity ◽  
Statistical Tests ◽  
Computer Aided Diagnosis ◽  
Discriminatory Power ◽  
Statistical Measures ◽  
Computer Aided ◽  
Size Bias ◽  
Aided Diagnosis ◽  
Probabilistic Diagnosis

Attention is focused on one important aspect of good performance in probabilistic diagnosis, the »reliability« (external validity) of probabilistic assertions: a diagnostic alternative claimed to be 90% certain, say, must occur neither more nor less than nine times out of ten on the average. Statistical measures are offered by which departures from such perfect reliability can be estimated, and statistical tests are developed in order to test the hypothesis of perfect reliability. The specific reliability defects looked for include overconfident diagnoses and so-called size bias (common diseases being over diagnosed). Reliability is contrasted with discriminatory power and other performance aspects. The illustrative data derive from a study of computer-aided diagnosis of the acute abdomen.

scholarly journals Hybrid Method of Local Binary Pattern and Classification Tree for Early Breast Cancer Detection by Mammogram Classification

2019 ◽  
Vol 8 (2S11) ◽  
pp. 139-145
Keyword(s):  
Breast Cancer ◽  
Decision Tree ◽  
Local Binary Pattern ◽  
Computer Aided Diagnosis ◽  
Decision Tree Classification ◽  
Statistical Measures ◽  
Computer Aided ◽  
Uniform Local Binary Pattern ◽  
Breast Tissues ◽  
Aided Diagnosis

- Breast cancer is an alarming disease due to a mutation in breast cells and it is one type of cancer among women which highly leads to their death. One of the most effective tools for early detection of breast cancer is mammography, which is a screening tool used to examine the human breast by using low-dose amplitude X-rays. Computer-Aided Diagnosis (CAD) is used as an important tool to help the medical professionals for classifying breast tissues into a different class. Computer-Aided Diagnosis (CAD) can be used to reduce human error in reading the mammograms and it shows effective results in the classification of benign and malignant abnormalities. The proposed method presents a new classification approach to detect the abnormalities in mammograms using Local Binary Pattern and Decision Tree Classification. A Uniform Local Binary Pattern(uLBP) is an extension of the original Local Binary Pattern in which only patterns that contain at most two transitions from 0 to 1 (or vice versa) are considered. In uniform Local Binary Pattern (LBP) mapping, there is a separate output label for each uniform pattern and all then on uniform patterns are assigned to a single label. These patterns are utilized to detect breast cancer by classification employing the Decision Tree Classification. Specificity and sensitivity are the two statistical measures used in this proposed method to verify and measure the significance of the test related to abnormalities in the breast tissues. Thus, it can be a measurement of performance tests for classifying the patients who do and do not suffer from cancer. The mini-MIAS mammography database is employed for testing the accuracy of the proposed method and the results are promising.

Pattern-Recognition: A Comparison of the Performance of Clinicians and Non-Clinicians - with a Note on the Performance of a Computer-Based System

1972 ◽  
Vol 11 (01) ◽  
pp. 32-37 ◽  
Author(s):  
F. T. DE DOMBAL ◽  
J. C. HORROCKS ◽  
J. R. STANILAND ◽  
P. J. GUILLOU
Keyword(s):  
Pattern Recognition ◽  
Computer System ◽  
Computer Aided Diagnosis ◽  
Clinical Interview ◽  
Computer Performance ◽  
Pattern Size ◽  
Computer Aided ◽  
Computer Based ◽  
Aided Diagnosis ◽  
Better Than

This paper describes a series of 10,500 attempts at »pattern-recognition« by two groups of humans and a computer based system. There was little difference between the performances of 11 clinicians and 11 other persons of comparable intellectual capability. Both groups’ performances were related to the pattern-size, the accuracy diminishing rapidly as the patterns grew larger. By contrast the computer system increased its accuracy as the patterns increased in size.It is suggested (a) that clinicians are very little better than others at pattem-recognition, (b) that the clinician is incapable of analysing on a probabilistic basis the data he collects during a traditional clinical interview and examination and (c) that the study emphasises once again a major difference between human and computer performance. The implications as - regards human- and computer-aided diagnosis are discussed.

COMPUTER-AIDED DIAGNOSIS OF GASTRIC LESIONS USING MAGNIFYING NARROW BAND IMAGING ENDOSCOPY

2019 ◽  
Author(s):  
S Kashin ◽  
R Kuvaev ◽  
E Kraynova ◽  
H Edelsbrunner ◽  
O Dunaeva ◽  
...  
Keyword(s):  
Narrow Band ◽  
Narrow Band Imaging ◽  
Computer Aided Diagnosis ◽  
Gastric Lesions ◽  
Computer Aided ◽  
Aided Diagnosis
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