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Multi-Class Breast Cancer Classification using Deep Learning Convolutional Neural Network

Majid Nawaz, Adel A., Taysir Hassan
2018 International Journal of Advanced Computer Science and Applications  
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  
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

Andreas S. Panayides, Amir Amini, Nenad Filipovic, Ashish Sharma, Sotirios Tsaftaris, Alistair Young, David J. Foran, Nhan Do, Spyretta Golemati, Tahsin Kurc, Kun Huang, Konstantina S. Nikita (+4 others)
2020 IEEE journal of biomedical and health informatics  
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

Li Z. Sha, Yi Ni Toh, Roger W. Remington, Yuhong V. Jiang
2020 Cognitive Research  
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

Matteo Manica, Joris Cadow, Roland Mathis, María Rodríguez Martínez
2019 npj Systems Biology and Applications  
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

Venkata Sunil Srikanth, S. Krithiga
2023 Intelligent Automation and Soft Computing  
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

Taye Girma Debelee, Samuel Rahimeto Kebede, Friedhelm Schwenker, Zemene Matewos Shewarega
2020 Journal of Imaging  
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

Amira Hassan Abed, Essam M Shaaban
2021 International journal of advanced networking and applications  
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

Niyaz Yoosuf, José Fernández Navarro, Fredrik Salmén, Patrik L. Ståhl, Carsten O. Daub
2020 Breast Cancer Research  
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

Nupur Biswas, Saikat Chakrabarti
2020 Frontiers in Oncology  
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]

Bryar Shareef, Min Xian, Aleksandar Vakanski
2020 arXiv   pre-print
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

Moses E. Ekpenyong, Mercy E. Edoho, Ifiok J. Udo, Philip I. Etebong, Nseobong P. Uto, Tenderwealth C. Jackson, Nkem M. Obiakor
2021 Informatics in Medicine Unlocked  
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

Apekshya Chhetri, Xin Li, Joseph V. Rispoli
2020 Frontiers in Medicine  
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

Muhammad Firoz Mridha, Md. Abdul Hamid, Muhammad Mostafa Monowar, Ashfia Jannat Keya, Abu Quwsar Ohi, Md. Rashedul Islam, Jong-Myon Kim
2021 Cancers  
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  
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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