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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Towards an approach using grammars for automatic classification of masses in mammograms

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Autor(es):
Dias Pedro, Ricardo Wandre [1] ; Machado-Lima, Ariane [2] ; Nunes, Fatima L. S. [1, 2]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Polytech Sch, Elect Engn, Sao Paulo - Brazil
[2] Univ Sao Paulo, Sch Arts, Sci & Humanities, Informat Syst, Sao Paulo, SP - Brazil
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: COMPUTATIONAL INTELLIGENCE; APR 2020.
Citações Web of Science: 0
Resumo

Approximately 15% of all cancer deaths among women worldwide is due to breast cancer. Mammography is one of the most useful methods for the early detection of this disease. Over the last decade, several papers were published reporting the usage of different computer-aided diagnosis systems using pattern recognition techniques as a second opinion to obtain a more accurate diagnosis. However, the theory of formal languages has not been explored in this field. In this context, the main contribution of this study is to present the usage of a new syntactic approach that is able to classify breast masses found in mammograms as benign or malignant. The experimental tests were performed using a dataset that contains 111 images from different sources. The grammar-based classifiers achieved accuracy values ranging from 89% to 100% depending on the features and the model employed. Furthermore, to achieve a feature dimension reduction, a feature selection technique based on the Gini importance of each feature was employed. Additionally, we compared the obtained results with the grammar-based classifiers to the more traditional classifiers used in this research area, such as artificial neural networks, support vector machines, k-nearest neighbors, and random forest. The best result achieved by the grammar-based classifiers was approximately 10% higher, in terms of accuracy, than the best results produced by the traditional classifiers, showing the strength of this grammatical approach. (AU)

Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
Beneficiário:Roberto Marcondes Cesar Junior
Linha de fomento: Auxílio à Pesquisa - Temático