TY - GEN
T1 - Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach
AU - Rodríguez-Esparza, Erick
AU - Zanella-Calzada, Laura A.
AU - Oliva, Diego
AU - Pérez-Cisneros, Marco
PY - 2020
Y1 - 2020
N2 - Breast cancer represents the most common type of cancer worldwide among women. One of the most important diagnostic methods of this disease are mammograms, however, the high prevalence of breast cancer has not been reduced due to the incorrect diagnosis of these images, since they can be complex to interpret. An approach that represents a fundamental process for the improvement of this diagnosis is digital image processing, since it can facilitate the interpretation of the images for the specialists. In this work is proposed the implementation of a new multilevel segmentation approach based on the minimum cross-entropy threshold - Harris Hawks Optimization (MCET-HHO) metaheuristic algorithm, identifying regions within the breast that have abnormal tissue. Then, these regions are subjected to an automatic classification system based on a bag-of-visual-words (BoVW) approach to identify healthy tissue, benign tumors, and malignant tumors. According to the results, the classifier reached an average accuracy of 0.86 in the training stage and 0.73 in the testing, proving to be statistically significant in the automatic classification of mammograms, presenting a preliminary tool for the support of specialists in the diagnosis of mammography images.
AB - Breast cancer represents the most common type of cancer worldwide among women. One of the most important diagnostic methods of this disease are mammograms, however, the high prevalence of breast cancer has not been reduced due to the incorrect diagnosis of these images, since they can be complex to interpret. An approach that represents a fundamental process for the improvement of this diagnosis is digital image processing, since it can facilitate the interpretation of the images for the specialists. In this work is proposed the implementation of a new multilevel segmentation approach based on the minimum cross-entropy threshold - Harris Hawks Optimization (MCET-HHO) metaheuristic algorithm, identifying regions within the breast that have abnormal tissue. Then, these regions are subjected to an automatic classification system based on a bag-of-visual-words (BoVW) approach to identify healthy tissue, benign tumors, and malignant tumors. According to the results, the classifier reached an average accuracy of 0.86 in the training stage and 0.73 in the testing, proving to be statistically significant in the automatic classification of mammograms, presenting a preliminary tool for the support of specialists in the diagnosis of mammography images.
KW - bag-of-visual-words.
KW - breast tumor
KW - classication
KW - digital image processing
KW - mammograms
KW - mcet-hho algorithm
UR - http://www.scopus.com/inward/record.url?scp=85085528722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085528722&partnerID=8YFLogxK
U2 - 10.1117/12.2549899
DO - 10.1117/12.2549899
M3 - Conference contribution
AN - SCOPUS:85085528722
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2020
A2 - Hahn, Horst K.
A2 - Mazurowski, Maciej A.
PB - SPIE
T2 - Medical Imaging 2020: Computer-Aided Diagnosis
Y2 - 16 February 2020 through 19 February 2020
ER -