Breast cancer histopathological image classification using Convolutional Neural Networks
Universidade Federal do Paraná · Universidade Tecnológica Federal do Paraná · +2 more institutions
Abstract
The performance of most conventional classification systems relies on appropriate data representation and much of the efforts are dedicated to feature engineering, a difficult and time-consuming process that uses prior expert domain knowledge of the data to create useful features. On the other hand, deep learning can extract and organize the discriminative information from the data, not requiring the design of feature extractors by a domain expert. Convolutional Neural Networks (CNNs) are a particular type of deep, feedforward network that have gained attention from research community and industry, achieving empirical successes in tasks such as speech recognition, signal processing, object recognition, natural…
Citation impact
- FWCI
- 94.54
- Percentile
- 100%
- References
- 38
Authors
4- FAFábio Alexandre SpanholCorresponding
Universidade Federal do Paraná, Universidade Tecnológica Federal do Paraná
- LSLuiz S. Oliveira
Universidade Federal do Paraná
- CPCaroline Petitjean
Université de Rouen Normandie, Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes
- LHLaurent Heutte
Universidade Federal do Paraná
Topics & keywords
- Computer science
- Artificial intelligence
- Convolutional neural network
- Deep learning
- Feature extraction
- Discriminative model
- Machine learning
- Pattern recognition (psychology)
- Reduced inequalities