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Classification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networks [article]

Elham Yousef Kalaf, Ata Jodeiri, Seyed Kamaledin Setarehdan, Ng Wei Lin, Kartini Binti Rahman, Nur Aishah Taib, Sarinder Kaur Dhillon
2021 arXiv   pre-print
The leverage in deep convolutional neural network approaches provided solutions in efficient analysis of breast ultrasound images.  ...  In this study, we proposed a new framework for classification of breast cancer lesions by use of an attention module in modified VGG16 architecture.  ...  Modifications such as additional attention block, different dense layers and ensembled loss functions were made. One of the improvements in the CNN models was the use of ensembled loss functions.  ... 
arXiv:2102.11519v1 fatcat:3vpma4atuvfnpp2he44bnqwtri

Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks

Elham Yousef Kalafi, Ata Jodeiri, Seyed Kamaledin Setarehdan, Ng Wei Lin, Kartini Rahmat, Nur Aishah Taib, Mogana Darshini Ganggayah, Sarinder Kaur Dhillon
2021 Diagnostics  
The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images.  ...  The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/diagnostics11101859 pmid:34679557 fatcat:ak2q6u47rrda3eosxyxhcsfh5y

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
314 Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound 316 SPNet: Shape Prediction using a Fully Convolutional Neural Network 317 Modeling Longitudinal  ...  for Histopathology 351 Combining Convolutional and Recurrent Neural Networks for Classification of Focal Liver Lesions in Multi-Phase CT Images 352 Domain and Geometry Agnostic CNNs for Left Atrium Segmentation  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound [article]

Gavriel Habib, Nahum Kiryati, Miri Sklair-Levy, Anat Shalmon, Osnat Halshtok Neiman, Renata Faermann Weidenfeld, Yael Yagil, Eli Konen, Arnaldo Mayer
2020 arXiv   pre-print
In this work, we propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images.  ...  Mammography and ultrasound are extensively used by radiologists as complementary modalities to achieve better performance in breast cancer diagnosis.  ...  In this paper, we propose a novel deep-learning method for the classification of breast lesion, using both mammography and ultrasound images of the lesion.  ... 
arXiv:2009.11009v1 fatcat:eqlhd4rcu5fjbdgvo5opzb7bcu

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez, Vasudevan Lakshminarayanan
2020 Applied Sciences  
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images.  ...  It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of  ...  Convolutional Neural Networks CNNs are the most widely used Neural Networks when it comes to DL and medical image analysis.  ... 
doi:10.3390/app10228298 fatcat:3m7jxe5rjvhedhp33ryoduqxbi

Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model

Zhencun Jiang, Zhengxin Dong, Lingyang Wang, Wenping Jiang, Suresh Manic
2021 Computational Intelligence and Neuroscience  
The ViT-CNN ensemble model is an ensemble model that combines the vision transformer model and convolutional neural network (CNN) model.  ...  a new balanced data set, and use the symmetric cross-entropy loss function to reduce the impact of noise in the data set.  ...  Acknowledgments e authors would like to thank SBILab for the data and the donations of 73 volunteers. is work was supported by the Natural National Science Foundation of China (81827807, 61675134, and  ... 
doi:10.1155/2021/7529893 pmid:34471407 pmcid:PMC8405335 fatcat:bzieizah5zgnjnopvkyi56unay

Artificial Intelligence-Based Breast Cancer Diagnosis Using Ultrasound Images and Grid-Based Deep Feature Generator

Haixia Liu, Guozhong Cui, Yi Luo, Yajie Guo, Lianli Zhao, Yueheng Wang, Abdulhamit Subasi, Sengul Dogan, Turker Tuncer
2022 International Journal of General Medicine  
Finally, these features are classified by using a deep neural network (DNN).  ...  This work presents a new deep feature generation technique for breast cancer detection using BUS images.  ...  They used 348 images of 238 patients and obtained patient-based sensitivity of 78.00%. Liao et al 18 used a convolutional neural network for breast tumor detection using BUS images.  ... 
doi:10.2147/ijgm.s347491 pmid:35256855 pmcid:PMC8898057 fatcat:aciuw7vdrzhtpo4pfgqlefrcam

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review

Ye-Jiao Mao, Hyo-Jung Lim, Ming Ni, Wai-Hin Yan, Duo Wai-Chi Wong, James Chung-Wai Cheung
2022 Cancers  
Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening.  ...  Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.  ...  Acknowledgments: Icons of the graphical abstract were royalty-free and extracted from Flaticon (, accessed on 20 December 2021) and Freepik (https://www.freepik. com/, accessed  ... 
doi:10.3390/cancers14020367 pmid:35053531 pmcid:PMC8773731 fatcat:iagxt7ctrnebtovrxll3tytu3q

Artificial Intelligence in Quantitative Ultrasound Imaging: A Review [article]

Boran Zhou, Xiaofeng Yang, Tian Liu
2020 arXiv   pre-print
In recent years, there has been an increasing interest in artificial intelligence (AI) applications in ultrasound imaging. However, no research has been found that surveyed the AI use in QUS.  ...  Quantitative ultrasound (QUS) imaging is a reliable, fast and inexpensive technique to extract physically descriptive parameters for assessing pathologies.  ...  Wang et al. developed a 3D deep neural network coupled with attention modules for prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the CNN  ... 
arXiv:2003.11658v1 fatcat:iujuh7gra5ax7od2gxoo6yrbpe

Deep Learning Based Computer-Aided Systems for Breast Cancer Imaging : A Critical Review [article]

Yuliana Jiménez-Gaona, María José Rodríguez-Álvarez, Vasudevan Lakshminarayanan
2020 arXiv   pre-print
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images.  ...  It also summarizes recent advances in computer-aided diagnosis (CAD) systems, which make use of new deep learning methods to automatically recognize images and improve the accuracy of diagnosis made by  ...  Acknwoledgments: VL would like to acknowledge support by a Discovery grant from the Natural Sciences and Engineering Research Council of Canada.  ... 
arXiv:2010.00961v1 fatcat:mrzh7mdlifduziuxqpokovueee

RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging [article]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
) model with minimal training parameters for tumor segmentation using breast ultrasound imaging to further improve the segmentation performance of varying tumor sizes.  ...  The RCA-IUnet model follows U-Net topology with residual inception depth-wise separable convolution and hybrid pooling (max pooling and spectral pooling) layers.  ...  Jemal, Cancer statistics, Computer-aided diagnosis of breast ultrasound images 2019, CA: a cancer journal for clinicians 69 (1) (2019) using transfer learning from deep convolutional neural  ... 
arXiv:2108.02508v4 fatcat:5n4zzp6zivhc3epubul6egvdha

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  
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  ...  Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities.  ...  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

Design Guidelines for Mammogram-based Computer-Aided Systems Using Deep Learning Techniques

Farnoosh Azour, Azzedine Boukerche
2022 IEEE Access  
Nevertheless, recently, the powerful application of Convolutional Neural Networks (CNN)s as one of the deep learning-based methods has revolutionized these systems' accuracy and development.  ...  Breast cancer is the second fatal disease among cancers patients both in Canada and across the globe. However, when detected early, a patients' survival rate can be raised.  ...  CONVOLUTIONAL NEURAL NETWORK Convolutional Neural Network (CNN) is the most impressive technique among all the different types of deep learning approaches for studying images.  ... 
doi:10.1109/access.2022.3151830 fatcat:roocprzhabba5pyujfxq3gsbxq

Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review [article]

Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal
2020 arXiv   pre-print
The advantages and limitations of different ANN models including spiking neural network (SNN), deep belief network (DBN), convolutional neural network (CNN), multilayer neural network (MLNN), stacked autoencoders  ...  Breast cancer is a common fatal disease for women. Early diagnosis and detection is necessary in order to improve the prognosis of breast cancer affected people.  ...  Performance of deep neural networks using the from-scratch training scenario Publicati on year Quantity of images The performance of multiple networks performed in [96] is summarized in Table 3  ... 
arXiv:2006.01767v1 fatcat:jjy3d2mgabfrrnpbkbyskfb2pi

BIRADS Features-Oriented Semi-supervised Deep Learning for Breast Ultrasound Computer-Aided Diagnosis [article]

Erlei Zhang, Stephen Seiler, Mingli Chen, Weiguo Lu, Xuejun Gu
2019 arXiv   pre-print
Breast ultrasound (US) is an effective imaging modality for breast cancer detection and diagnosis.  ...  Then, the converted BFMs are used as the input of an SDL network, which performs unsupervised stacked convolutional auto-encoder (SCAE) image reconstruction guided by lesion classification.  ...  ACKNOWLEDGMENT The authors are grateful for the support in preparing the manuscript provided by Dr. Jonathan Feinberg.  ... 
arXiv:1904.01076v1 fatcat:lho2k4pd6bbldhwi37nlen3szm
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