TY  - CONF
SN  - 9781509055593
N1  - cited By 30; Conference of 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 ; Conference Date: 12 September 2017 Through 14 September 2017; Conference Code:132915
AV  - none
TI  - Mammogram classification using deep learning features
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041421925&doi=10.1109%2fICSIPA.2017.8120660&partnerID=40&md5=b01acf901c713f819bd940e593c55f7f
A1  - Gardezi, S.J.S.
A1  - Awais, M.
A1  - Faye, I.
A1  - Meriaudeau, F.
Y1  - 2017///
ID  - scholars9085
EP  - 488
KW  - Binary trees; Convolution; Image processing; Mammography; Neural networks; X ray screens
KW  -  10-fold cross-validation; Classification accuracy; Convolutional neural network; Feature matrices; Learning approach; Learning architectures; Mammogram classifications; VGG-16
KW  -  Deep learning
N2  - This paper presents a method for classification of normal and abnormal tissues in mammograms using a deep learning approach. VGG-16 CNN deep learning architecture with convolutional filter of (3�?3) is implemented on mammograms ROIs from the IRMA dataset. The deep feature matrix is computed from first fully connected layer. The results are evaluated using 10 fold cross validation on SVM, binary trees, simple logistics and KNN (with k=1, 3, 5) classifiers. The method produced 100 classification accuracies with AUC 1.0. © 2017 IEEE.
SP  - 485
PB  - Institute of Electrical and Electronics Engineers Inc.
ER  -