Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA-CNN)

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, M...

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Bibliographic Details
Published in:Journal of medical systems Vol. 44; no. 1; pp. 30 - 9
Main Authors: Agnes, S. Akila, Anitha, J., Pandian, S. Immanuel Alex, Peter, J. Dinesh
Format: Journal Article
Language:English
Published: New York Springer US 01.01.2020
Springer Nature B.V
Subjects:
Artificial neural networks
Breast cancer
Breast Neoplasms - pathology
Classification
Health Informatics
Health Informatics and Computer Vision
Health Sciences
Humans
Image & Signal Processing
Image classification
Image Interpretation, Computer-Assisted - methods
Machine Learning
Mammography
Mammography - methods
Medicine
Medicine & Public Health
Neural networks
Neural Networks, Computer
Recent Advances in Deep Learning for Biomedical Signal Processing
Statistics for Life Sciences
Feature learning
Breast cancer
Deep convolutional neural network
Computer-aided detection
Image classification
ISSN:0148-5598, 1573-689X, 1573-689X
Online Access:Get full text
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Summary: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.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1494-z