Diagnosis of breast tissue in mammography images based local feature descriptors

Breast cancer is one of the leading causes of death by cancer among women. The high mortality rates and the occurrence of this cancer worldwide show the importance of the investigation and development of means for the detection and early diagnosis of this disease. Computer-Aided Detection and Diagno...

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Bibliographic Details
Published in:Multimedia tools and applications 2019-05, Vol.78 (10), p.12961-12986
Main Authors: Matos, Caio Eduardo Falcão, Souza, Johnatan Carvalho, Diniz, João Otávio Bandeira, Junior, Geraldo Braz, de Paiva, Anselmo Cardoso, de Almeida, João Dallyson Sousa, da Rocha, Simara Vieira, Silva, Aristófanes Correa
Format: Article
Language:English
Subjects:
Accuracy
Breast cancer
Cancer
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Diagnosis
Diagnostic systems
Digitization
Feature extraction
Machine learning
Mammography
Medical imaging
Multimedia Information Systems
Special Purpose and Application-Based Systems
Support vector machines
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Summary:Breast cancer is one of the leading causes of death by cancer among women. The high mortality rates and the occurrence of this cancer worldwide show the importance of the investigation and development of means for the detection and early diagnosis of this disease. Computer-Aided Detection and Diagnosis systems have been developed to improve diagnostic accuracy by radiologists. This work proposes a method for discriminating patterns of malignancy and benignity of masses in digitized mammography images through the analysis of local features. The method comparatively applies the Scale-Invariant Feature Transform (SIFT), Speed Up Robust Feature (SURF), Oriented Fast and Rotated BRIEF (ORB) and Local Binary Pattern (LBP) descriptors for local feature extraction. These features are represented by a Bag of Features (BoF) model, applied to provide new representations of the data and to reduce its dimensionality. Finally, the features are used as input for the Support Vector Machine (SVM), Adaptive Boosting (Adaboost) and Random Forests (RF) classifiers to differentiate malignant and benign masses. The method obtained significant results, reaching 100% sensitivity, 99.65% accuracy and 99.24% specificity for benign and malignant mass classification.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-6390-x