Abstract：
:Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA-CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA-CNN is a convolutional neural network-based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini-MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA-CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC.
journal_name
J Med Systjournal_title
Journal of medical systemsauthors
Agnes SA,Anitha J,Pandian SIA,Peter JDdoi
10.1007/s10916-019-1494-zsubject
Has Abstractpub_date
2019-12-14 00:00:00pages
30issue
1eissn
0148-5598issn
1573-689Xpii
10.1007/s10916-019-1494-zjournal_volume
44pub_type
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