Towards an approach using grammars for automatic classification of masses in mammograms. (2nd April 2020)

• Record Type: Journal Article
• Authors: Dias Pedro, Ricardo Wandré; Machado‐Lima, Ariane; Nunes, Fátima L. S.
• Other Names: Ventura Sebastian guestEditor.; Soda Paolo guestEditor.; González Alejandro Rodríguez guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 4(2021)
• Page Start: 1515
• Page End: 1544
• Publication Date: 2020-04-02
• Subjects: breast cancer diagnosis -- grammars -- machine learning -- mammography -- mass classification -- syntactic analysis; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12320
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: 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.