Minimizing false negative rate in melanoma detection and providing insight into the causes of classification.
Ellák SomfaiBenjámin BaffyKristian FenechChanglu GuoRita HosszúDorina KorózsFabrizio NunnariMarcell PólikDaniel SonntagAttila UlbertAndrás LorinczPublished in: CoRR (2021)
Keyphrases
- false negative rate
- false negative
- false positives
- false positive rate
- detection rate
- robust detection
- automatic classification
- classification accuracy
- low false positive rate
- pattern recognition
- machine learning
- classification scheme
- class labels
- feature vectors
- decision trees
- digital mammograms
- detection algorithm
- detection method
- mammogram images
- feature extraction
- image classification
- training set
- support vector
- automated detection
- neyman pearson
- classification models
- pattern classification
- automatic detection
- computer aided
- classification algorithm
- support vector machine svm
- anomaly detection
- text classification
- supervised learning
- preprocessing
- feature selection