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Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network
2018
International Journal of Advanced Computer Science and Applications
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In this paper, we present a deep learning approach based on a Convolutional Neural Network (CNN) model for multi-class breast cancer classification. ...
In this context, the use of automatic image processing techniques resulting from deep learning represents a promising avenue for assisting in the diagnosis of breast cancer. ...
Much work has been directed towards the detection of the presence of cancerous tissue in the breast and the classification of tumors. ...
doi:10.14569/ijacsa.2018.090645
fatcat:cvf3m4zxg5cb5fkm5ast5lt7qu
Front Matter: Volume 10134
2017
Medical Imaging 2017: Computer-Aided Diagnosis
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using a Base 36 numbering system employing both numerals and letters. ...
SPIE uses a seven-digit CID article numbering system structured as follows: The first five digits correspond to the SPIE volume number. The last two digits indicate publication order within the volume ...
04 Bladder cancer treatment response assessment using deep learning in CT with transfer learning 10134 05 Convolutional neural network based deep-learning architecture for prostate cancer detection on ...
doi:10.1117/12.2277119
dblp:conf/micad/X17
fatcat:ika7pheqxngdxejyvkss4dkbv4
AI and Medical Imaging Informatics: Current Challenges and Future Directions
2020
IEEE journal of biomedical and health informatics
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It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already ...
This transfer learning approach is not straightforward, however, when the objective is tissue classification of 3D image data. ...
Toward this direction, transfer learning approaches and uptake in popular frameworks supported by a substantial community base has been catalytic. ...
doi:10.1109/jbhi.2020.2991043
pmid:32609615
pmcid:PMC8580417
fatcat:dcaefxwwqjfwla5asin34x2hxm
Perceptual learning in the identification of lung cancer in chest radiographs
2020
Cognitive Research
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Performance improved across training sessions, and notably, the improvement transferred to the classification of novel images. ...
To assess the nature of perceptual learning, test items were presented in three formats - the full image, the cutout of the tumor, or the background only. ...
Acknowledgments Images used in this study were obtained from the Japanese Society of Radiological Technology Database (Shiraishi et al., 2000) , accessible at http:// db.jsrt.or.jp/eng.php. ...
doi:10.1186/s41235-020-0208-x
pmid:32016647
pmcid:PMC6997313
fatcat:wkazf4x6ezhwhkpcwdtxm42p3u
PIMKL: Pathway-Induced Multiple Kernel Learning
2019
npj Systems Biology and Applications
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in transfer learning tasks. ...
PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm ...
Towards this end, we introduce the Pathway-Induced Multiple Kernel Learning (PIMKL), a supervised classification algorithm for phenotype prediction from molecular data that jointly exploits the benefits ...
doi:10.1038/s41540-019-0086-3
pmid:30854223
pmcid:PMC6401099
fatcat:nv7rall7crepdcnk6gwzyqwk3e
Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures
2023
Intelligent Automation and Soft Computing
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Deep neural network (DNN) based computer-aided breast tumor diagnosis (CABTD) method plays a vital role in the early detection and diagnosis of breast tumors. ...
It conveys the additional consideration of a local-ROI-structures for further enhancing the pre-trained DNN-based CABTD method's breast tumor diagnostic performance without degrading its visual quality ...
Moi Hoon Yap used this dataset to develop a deep-learning-based method for breast tumor segmentation [53, 54] . ...
doi:10.32604/iasc.2023.023474
fatcat:q3euhlnnzbhsfoub6bq4kom4xa
Deep Learning in Selected Cancers' Image Analysis—A Survey
2020
Journal of Imaging
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Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. ...
As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network ...
[91] proposed transfer learning and fully convolution network (FCN) to achieve robust tumor segmentation using VGG-16 networks. ...
doi:10.3390/jimaging6110121
pmid:34460565
fatcat:2xvx5uya25a23nxicq3hdl42hi
Modeling Deep Neural Networks For Breast Cancer Thermography Classification: A Review Study
2021
International journal of advanced networking and applications
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Building up a breast cancer screening platform is vital to encourage early "Breast cancer" detection and treatment. ...
CNNs can naturally group bosom thermograms into classifications, for example, ordinary and up-normal. ...
REVIEW ON BREAST CANCER USING THERMOGRAPHS AND DEEP LEARNING Recently as shown in table 1, in 2020, Samir S. & Shivajirao M. ...
doi:10.35444/ijana.2021.13209
fatcat:opz7rlcjr5hy3dnvqnhtof2nf4
Identification and transfer of spatial transcriptomics signatures for cancer diagnosis
2020
Breast Cancer Research
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Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types. ...
In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. ...
ST signature transfer of expert annotated breast cancer sections using a support vector machine Machine learning has been frequently used for cancer prediction and prognosis. ...
doi:10.1186/s13058-019-1242-9
pmid:31931856
pmcid:PMC6958738
fatcat:pe3bysiqf5f37pjcaleosnlbue
Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer
2020
Frontiers in Oncology
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Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the ...
Transfer learning, a branch of ML, provides opportunity to transfer the learned information from a well-studied species to an understudied species (82) . ...
HI-DFN Forest is used for
classification
Considers intrinsic
statistical properties and
learns high-level
representations of each
omics data. ...
doi:10.3389/fonc.2020.588221
pmid:33154949
pmcid:PMC7591760
fatcat:5kfd6jid6vcx7h4qvk52pngo5m
Stan: Small tumor-aware network for breast ultrasound image segmentation
[article]
2020
arXiv
pre-print
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Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. ...
Although deep learning-based approaches have been proposed and achieved promising results, existing approaches have difficulty in detecting small breast tumors. ...
[12] used transfer learning for classification of BUS images, however, the proposed model does not perform tumor segmentation. Similarly, Yap et al. ...
arXiv:2002.01034v1
fatcat:vmy5hfg3yvg6vaf4hba37lsebq
A Transfer Learning Approach to Drug Resistance Classification in Mixed HIV Dataset
2021
Informatics in Medicine Unlocked
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However, the transfer learning approach with DNN2 [9 7 3 1] configuration produced superior classification results when compared to other variants/configurations, with classification accuracy of 99.40% ...
This paper implements a transfer learning approach that classifies patients' response to failed treatments due to adverse drug reactions. ...
Deniz et al. [53]
To compare the classification of
transfer learning and deep feature
extraction on breast cancer
detection. ...
doi:10.1016/j.imu.2021.100568
fatcat:5ermpdm3tra3rhskhsgkopgrkm
Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer
2020
Frontiers in Medicine
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Clinically, magnetic resonance imaging (MRI) techniques are routinely used in determining benign and malignant tumor phenotypes and for monitoring treatment outcomes. ...
The preferred clinical procedure-dynamic contrast-enhanced MRI-illuminates the hypervascularity of breast tumors through a gadolinium-based contrast agent; however, accumulation of the potentially toxic ...
Machine learning has applications in breast lesion detection and classification, as well as predicting NAC response. ...
doi:10.3389/fmed.2020.00175
pmid:32478083
pmcid:PMC7235971
fatcat:c4nomtzogzbfxjwt3ivocfl3qm
A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis
2021
Cancers
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This review focuses on the evolving architectures of deep learning for breast cancer detection. ...
In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing ...
Additionally, special thanks are given to the Advanced Machine Learning lab, BUBT and the Computer Vision & Pattern Recognition Lab, UAP for providing facilities in which to research and publish. ...
doi:10.3390/cancers13236116
pmid:34885225
fatcat:ircywikuuvc25laiz3fsrc65bq
Front Matter: Volume 9785
2016
Medical Imaging 2016: Computer-Aided Diagnosis
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using a Base 36 numbering system employing both numerals and letters. ...
Vol. 9785 978501-4 Downloaded From: https://www.spiedigitallibrary.org/conference-proceedings-of-spie on 7/22/2018 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use ...
and characterization of microcalcifications on
mammography [9785-27]
9785 0T
Predicting Ki67% expression from DCE-MR images of breast tumors using textural kinetic
features in tumor habitats [9785 ...
doi:10.1117/12.2240961
dblp:conf/micad/X16
fatcat:b5addnksdrgp3ixwvbjt53xeqe
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