Transfer Learning in Breast Mammogram Abnormalities Classification With Mobilenet and Nasnet

Breast cancer has an important incidence in women mortality worldwide. Currently, mammography is considered the gold standard for breast abnormalities screening examinations since it aids in the early detection and diagnosis of the illness. However, both identification of mass lesions and its malign...

Full description

Saved in:
Bibliographic Details
Published inInternational Conference on Systems, Signals, and Image Processing (Online) pp. 109 - 114
Main Authors Falconi, Lenin G., Perez, Maria, Aguilar, Wilbert G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2019
Subjects
Breast
Cancer
Deep learning
digital mammogram
Feature extraction
image pre-processing
machine learning
Mammography
Solid modeling
Training
transfer learning
Online AccessGet full text

Cover

More Information
Summary:Breast cancer has an important incidence in women mortality worldwide. Currently, mammography is considered the gold standard for breast abnormalities screening examinations since it aids in the early detection and diagnosis of the illness. However, both identification of mass lesions and its malignancy classification is a challenging problem for artificial intelligence. Research has turned to the use of deep learning models in mammography which can enhance the performance of Computer Aided Diagnosis Systems (CADx). In this paper, we present our preliminary results on the use of transfer learning for malignancy classification of breast abnormality. We experiment with models that, according to our literature review, have not yet been explored thoroughly such as NasNet and MobileNet. Their performance is compared with InceptionV3 and Resnet50. The best results were obtained with Resnet50 and MobileNet with 78.4% and 74.3%, respectively. Also, some image pre-processing steps are studied in order to increase classification accuracy.
ISBN:1728132533
9781728132532
ISSN:2157-8702
DOI:10.1109/IWSSIP.2019.8787295