The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Breast cancer detection using deep convolutional neural networks and support vector machines
Open accessAbstract
It is important to detect breast cancer as early as possible. In this manuscript, a new methodology for classifying breast cancer using deep learning and some segmentation techniques are introduced. A new computer aided detection (CAD) system is proposed for classifying benign and malignant mass tumors in breast mammography images. In this CAD system, two segmentation approaches are used. The first approach involves determining the region of interest (ROI) manually, while the second approach uses the technique of threshold and region based. The deep convolutional neural network (DCNN) is used for feature extraction. A well-known DCNN architecture named AlexNet is used and is fine-tuned to classify two classes instead of 1,000 classes. The last fully connected (fc) layer is connected to the support vector machine (SVM) classifier to obtain better accuracy. The results are obtained using the following publicly available datasets (1) the digital database for screening mammography (DDSM); and (2) the Curated Breast Imaging Subset of DDSM (CBIS-DDSM). Training on a large number of data gives high accuracy rate. Nevertheless, the biomedical datasets contain a relatively small number of samples due to limited patient volume. Accordingly, data augmentation is a method for increasing the size of the input data by generating new data from the original input data. There are many forms for the data augmentation; the one used here is the rotation. The accuracy of the new-trained DCNN architecture is 71.01% when cropping the ROI manually from the mammogram. The highest area under the curve (AUC) achieved was 0.88 (88%) for the samples obtained from both segmentation techniques. Moreover, when using the samples obtained from the CBIS-DDSM, the accuracy of the DCNN is increased to 73.6%. Consequently, the SVM accuracy becomes 87.2% with an AUC equaling to 0.94 (94%). This is the highest AUC value compared to previous work using the same conditions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,395
Adaptive histogram equalization and its variations
Pizer, S. M., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., ter Haar Romeny, B., Zimmerman, J. B., & Zuiderveld, K. (1987) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(87)80186-x · Cited by 4,012
Support Vector Machines for Classification and Regression
Gunn, S. R. (1998) · Cited by 2,820
ImageNet: A large-scale hierarchical image database
Deng, J. H., Dong, W. F., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206848 · Cited by 2,714
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
Convolutional networks and applications in vision
LeCun, Y., Kavukcuoglu, K., & Farabet, C. (2010) · Proceedings of 2010 IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.2010.5537907 · Cited by 2,213
THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY
Heath, M. D., Bowyer, K. W., Kopans, D. B., & Moore, R. M. (2007) · Cited by 1,017
Breast cancer histopathological image classification using Convolutional Neural Networks
Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016) · 2016 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727519 · Cited by 942
A curated mammography data set for use in computer-aided detection and diagnosis research
Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017) · Scientific Data · doi:10.1038/sdata.2017.177 · Cited by 859
ImageNet classification with deep convolutional neural networks
Krizhevsky (2012) · Medical Entomology and Zoology · Cited by 799
Pisano, E. D., Zong, S., Hemminger, B. M., DeLuca, M., Johnston, R. E., Muller, K., Braeuning, M. P., & Pizer, S. M. (1998) · Journal of Digital Imaging · doi:10.1007/bf03178082 · Cited by 766
Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances
Tang, J., Rangayyan, R. M., Xu, J., El Naqa, I., & Yang, Y. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.2009441 · Cited by 619
Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2482920 · Cited by 608 · cited 1×
“There are many CNN architectures such as CiFarNet ( Krizhevsky, 2009 ; Roth et al., 2016 ), AlexNet ( Krizhevsky, Sutskever & Hinton, 2012 ), GoogLeNet ( Szegedy et al., 2015 ), The AlexNet architecture achieved significantly better performance over the other deep learning methods for ImageNet…”
MethodA support vector machine approach for detection of microcalcifications
El-Naqa, I., Yang, Y., Wernick, M. N., Galatsanos, N. P., & Nishikawa, R. M. (2002) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2002.806569 · Cited by 571
Huynh, B. Q., Li, H., & Giger, M. L. (2016) · Journal of Medical Imaging · doi:10.1117/1.jmi.3.3.034501 · Cited by 539
Background Prior-Based Salient Object Detection via Deep Reconstruction Residual
Han, J., Zhang, D., Hu, X., Guo, L., Ren, J., & Wu, F. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2381471 · Cited by 444
Self-citationSahiner, B., Chan, H.-P., Petrick, N., Wei, D., Helvie, M. A., Adler, D. D., & Goodsitt, M. M. (1996) · IEEE Transactions on Medical Imaging · doi:10.1109/42.538937 · Cited by 420
Zabalza, J., Ren, J., Zheng, J., Zhao, H., Qing, C., Yang, Z., Du, P., & Marshall, S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.11.044 · Cited by 389
Self-citationA deep learning approach for the analysis of masses in mammograms with minimal user intervention
Dhungel, N., Carneiro, G., & Bradley, A. P. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.01.009 · Cited by 336
Günther, J., Pilarski, P. M., Helfrich, G., Shen, H., & Diepold, K. (2014) · Procedia Technology · doi:10.1016/j.protcy.2014.09.007 · Cited by 303
Deep Learning and Convolutional Neural Networks for Medical Image Computing
(2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-42999-1 · Cited by 289
Li, X., Chiu, C.-Y., Wang, H.-J., Kassemeyer, S., Botha, S., Shoeman, R. L., Lawrence, R. M., Kupitz, C., Kirian, R., James, D., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.154 · Cited by 251
Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests
Dhungel, N., Carneiro, G., & Bradley, A. P. (2015) · 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2015.7371234 · Cited by 228
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Zhu, W., Lou, Q., Vang, Y. S., & Xie, X. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66179-7_69 · Cited by 214
Zabalza, J., Ren, J., Zheng, J., Han, J., Zhao, H., Li, S., & Marshall, S. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2398468 · Cited by 166
Self-citationThe Automated Learning of Deep Features for Breast Mass Classification from Mammograms
Dhungel, N., Carneiro, G., & Bradley, A. P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_13 · Cited by 141
Qiao, T., Ren, J., Wang, Z., Zabalza, J., Sun, M., Zhao, H., Shutao Li, A., Benediktsson, J. A., Dai, Q., & Marshall, S. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2598065 · Cited by 131
Self-citationMass detection using deep convolutional neural network for mammographic computer-aided diagnosis
Suzuki, S., Zhang, X., Homma, N., Ichiji, K., Sugita, N., Kawasumi, Y., Ishibashi, T., & Yoshizawa, M. (2016) · Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) · doi:10.1109/sice.2016.7749265 · Cited by 101
Breast mass lesion classification in mammograms by transfer learning
Jiang, F., Liu, H., Yu, S., & Xie, Y. (2017) · International Conference on Bioinformatics and Computational Biology (ICBCB) · doi:10.1145/3035012.3035022 · Cited by 89
Saraswathi, D., & Emperumal, S. (2017) · IET Computer Vision · doi:10.1049/iet-cvi.2016.0425 · Cited by 81
Combining deep convolutional networks and SVMs for mass detection on digital mammograms
Wichakam, I., & Vateekul, P. (2016) · International Conference on Knowledge and Smart Technology · doi:10.1109/kst.2016.7440527 · Cited by 39
Cited by
A scoping review of transfer learning research on medical image analysis using ImageNet
Morid, M. A., Borjali, A., & Del Fiol, G. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104115 · Cited by 508
Ibrahim, D. M., Elshennawy, N. M., & Sarhan, A. M. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104348 · Cited by 401
A systematic review of artificial intelligence techniques in cancer prediction and diagnosis
Kumar, Y., Gupta, S., Singla, R., & Hu, Y.-C. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09648-w · Cited by 223
Convolution Neural Network for Breast Cancer Detection and Classification Using Deep Learning
Abunasser, B., AL-Hiealy, M. R., Zaqout, I., & Abu-Naser, S. (2023) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2023.24.2.531 · Cited by 174
Targeted Self Supervision For Classification On A Small Covid-19 Ct Scan Dataset
Ewen, N., & Khan, N. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434047 · Cited by 29
Metadata
- Authors
- Ragab, D. A., Sharkas, M., Marshall, S., & Ren, J.
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2019-01-28
- Volume
- 7
- Pages
- e6201
- ISSN
- 2167-8359 (ISSN-L)
- OpenAlex
- W2908052439
- Cited by
- 513
- References
- 33
- FWCI
- 27.05
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
