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DimensionsGardezi, S.J.S. and Awais, M. and Faye, I. and Meriaudeau, F. (2017) Mammogram classification using deep learning features. In: UNSPECIFIED.
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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....
Abstract
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.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Additional Information: | 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 |
| Uncontrolled Keywords: | Binary trees; Convolution; Image processing; Mammography; Neural networks; X ray screens, 10-fold cross-validation; Classification accuracy; Convolutional neural network; Feature matrices; Learning approach; Learning architectures; Mammogram classifications; VGG-16, Deep learning |
| Depositing User: | Mr Ahmad Suhairi UTP |
| Date Deposited: | 09 Nov 2023 16:21 |
| Last Modified: | 09 Nov 2023 16:21 |
| URI: | https://khub.utp.edu.my/scholars/id/eprint/9085 |
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