Name
Abstract | ||
|---|---|---|
Computer-aided diagnosis (CAD) has great potential in providing real benefits to doctors and patients. Recent studies have, however, found lack of trust in CAD by radiologists in clinical diagnostic decision making. One of the main reasons is the lack of an appropriate confidence measure. This paper presents the first-ever study of classification confidence in the context of breast mass classification. We evaluated 11 state-of-the-art classification algorithms on breast mass image data using their confidence of classification metric, in addition to other standard evaluation metrics including accuracy and area under the curve (ROC). Experimental results show that although most classifiers produced very similar results with less than 2% difference in terms of accuracy and ROC, their performances are significantly different in terms of confidence levels. We suggest that the confidence measure should be used in conjunction with the existing performance metrics such as accuracy and ROC. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Confidence Level, Breast Mass Classification, Computer Aided Diagnosis, Machine Learning |
Field | DocType | ISSN |
CAD,Pattern recognition,Mass classification,Computer science,Support vector machine,Artificial intelligence,Statistical classification,Contextual image classification | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrik Rampun | 1 | 46 | 8.38 |
| hui wang | 2 | 76 | 17.01 |
| Bryan W. Scotney | 3 | 670 | 82.50 |
| Philip J. Morrow | 4 | 384 | 53.29 |
| Reyer Zwiggelaar | 5 | 711 | 103.74 |