relation: https://khub.utp.edu.my/scholars/9085/
title: Mammogram classification using deep learning features
creator: Gardezi, S.J.S.
creator: Awais, M.
creator: Faye, I.
creator: Meriaudeau, F.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2017
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Gardezi, S.J.S. and Awais, M. and Faye, I. and Meriaudeau, F.  (2017) Mammogram classification using deep learning features.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041421925&doi=10.1109%2fICSIPA.2017.8120660&partnerID=40&md5=b01acf901c713f819bd940e593c55f7f
relation: 10.1109/ICSIPA.2017.8120660
identifier: 10.1109/ICSIPA.2017.8120660