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Breast cancer diagnosis from histopathological images using textural features and CBIR.
Metadata
Journalartificial intelligence in medicine4.383Date
2020 Apr 22
5 months ago
Type
Journal Article
Volume
2020-05 / 105 : 101845
Author
Carvalho ED 1, Filho AOC 2, Silva RRV 3, Araújo FHD 4, Diniz JOB 5, Silva AC 6, Paiva AC 7, Gattass M 8
Affiliation
  • 2. Federal University of Piauí - UFPI, Picos, PI, Brazil. Electronic address: [email protected]
  • 3. Federal University of Piauí - UFPI, Picos, PI, Brazil. Electronic address: [email protected]
  • 4. Federal University of Piauí - UFPI, Picos, PI, Brazil. Electronic address: [email protected]
  • 5. Federal Institute of Education, Science and Technology of Maranhão - IFMA, Grajaú, MA, Brazil; Federal University of Maranhão - UFMA, São Luís, MA, Brazil. Electronic address: [email protected]
  • 6. Federal University of Maranhão - UFMA, São Luís, MA, Brazil. Electronic address: [email protected]
  • 7. Federal University of Maranhão - UFMA, São Luís, MA, Brazil. Electronic address: [email protected]
  • 8. Pontifical Catholic University of Rio de Janeiro - PUC - Rio, Rio de Janeiro, RJ, Brazil. Electronic address: [email protected]
Doi
PMIDMESH
Abstract
Currently, breast cancer diagnosis is an extensively researched topic. An effective method to diagnose breast cancer is to use histopathological images. However, extracting features from these images is a challenging task. Thus, we propose a method that uses phylogenetic diversity indexes to characterize images for creating a model to classify histopathological breast images into four classes - invasive carcinoma, in situ carcinoma, normal tissue, and benign lesion. The classifiers used were the most robust ones according to the existing literature: XGBoost, random forest, multilayer perceptron, and support vector machine. Moreover, we performed content-based image retrieval to confirm the classification results and suggest a ranking for sets of images that were not labeled. The results obtained were considerably robust and proved to be effective for the composition of a CADx system to help specialists at large medical centers.
Keywords: Breast cancer Computer-aided diagnosis Content-based image retrieval Histopathological images Medical images
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Artif Intell Medartificial intelligence in medicine
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