Open AccessDeep learning algorithm for breast masses classification in mammograms
Author(s) -
Gnanasekaran Vaira Suganthi,
Joypaul Sutha,
Meenakshi Sundaram Parvathy,
Chairman Durga Devi
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0070
Subject(s) - artificial intelligence , computer science , convolutional neural network , mammography , pattern recognition (psychology) , deep learning , classifier (uml) , pooling , contextual image classification , breast cancer , machine learning , image (mathematics) , cancer , medicine
A mammogram is an image of a breast used to detect and diagnose breast cancer. This paper emphases a Computer‐Aided Detection system based on convolutional neural network (CNN) that uses the concept of deep learning to classify the mammogram images into benign, malignant and normal. The proposed CNN model consists of eight convolutional, four max‐pooling and two fully connected layers and achieved better results compared to the pre‐trained nets, AlexNet and VGG16. The proposed model demonstrates the feasibility of using CNNs on medical image processing techniques for the classification of breast masses. The results are also compared with the state‐of‐the‐art machine learning algorithm like kNN classifier. Experimentation is done with three datasets. Among them, two are publicly available, Mammographic Image Analysis Society (MIAS), digital database for screening mammography (DDSM) and an internally collected dataset. The proposed model achieved accuracies of 92.54, 96.47 and 95 and the Area under the ROC curve (AUC) score of 0.85, 0.96 and 0.94 for MIAS, DDSM and the internally collected dataset respectively. Furthermore, the images of the three datasets are merged to build one large set and used to fine tune the proposed CNN model and produced accuracy of 98.32 and AUC of 0.98.