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Application of Artificial Neural Network Models in Segmentation and Classification of Nodules in Breast Ultrasound Digital Images

Karem D. Marcomini, Antonio A. O. Carneiro, Homero Schiabel
2016 International Journal of Biomedical Imaging  
We performed the tests in ultrasound images obtained from breast phantoms made of tissue mimicking material.  ...  To evaluating the data generalization, the classification was performed with a group of unknown images to the system, both to simulators and to clinical trials, resulting in an accuracy of 90% and 81%,  ...  providing the US clinical images; and to Dr.  ... 
doi:10.1155/2016/7987212 pmid:27413361 pmcid:PMC4927983 fatcat:huczu2twwbfqlbiw433f2quv2e

Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging

Xiaowen Liang, Jinsui Yu, Jianyi Liao, Zhiyi Chen
2020 BioMed Research International  
The training and validation sets comprised randomly selected thyroid and breast nodule images.  ...  The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model.  ...  e authors thank Shengwen Guo, in whose lab some of these experiments were performed. e authors also thank Yupeng Wu and Congling Wu for helpful conversations.  ... 
doi:10.1155/2020/1763803 pmid:32420322 pmcid:PMC7199615 fatcat:5tgvycecxbhjnaonmwzmidqqyy

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  09 3D convolutional neural network for automatic detection of lung nodules in chest CT 10134 0A Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer

M. M. Mehdy, P. Y. Ng, E. F. Shair, N. I. Md Saleh, C. Gomes
2017 Computational and Mathematical Methods in Medicine  
Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection.  ...  Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer.  ...  Acknowledgments This study has been supported by the Departments of Computer and Communication Engineering, Electrical and Electronics Engineering and Chemical and Environmental Engineering at Universiti  ... 
doi:10.1155/2017/2610628 pmid:28473865 pmcid:PMC5394406 fatcat:25zbqzges5ahjh6dmswxomxp6q

Computer-aided Diagnosis Using Neural Networks and Support Vector Machines for Breast Ultrasonography

Yu-Len Huang
2009 Journal of Medical Ultrasound  
This article will review the applications of neural network and SVM in the current breast CAD systems of ultrasound.  ...  The artificial neural networks and support vector machines (SVMs) models are extensively used in classification for its ability to model the complex system.  ...  Neural network and SVM were widely used artificial intelligence models in image classification.  ... 
doi:10.1016/s0929-6441(09)60011-4 fatcat:4uwkaldimjbitmp2y2rn4igzaa

Microscopic Tumour Classification by Digital Mammography

Jingjing Yang, Huichao Li, Ning Shi, Qifan Zhang, Yanan Liu, Zhihan Lv
2021 Journal of Healthcare Engineering  
In this paper, we investigate the classification of microscopic tumours using full digital mammography images.  ...  optimize the network training to achieve a fine segmentation of the lesion area, and demonstrate the accuracy and feasibility of the two models in medical image segmentation.  ...  clinical application and achieved the cross-application of deep learning and medical image segmentation.  ... 
doi:10.1155/2021/6635947 pmid:33613927 pmcid:PMC7878100 fatcat:2wvyutuacbcx7gg53srefddpam

Computer-aided Diagnosis in Breast Ultrasound

Dar-Ren Chen, Yi-Hsuan Hsiao
2008 Journal of Medical Ultrasound  
Mammography and ultrasound (US) are the main imaging techniques used in the detection of breast cancer.  ...  systems have expanded the clinical application of breast US.  ...  The authors concluded that SVM was helpful in the image diagnosis of breast cancer, and the classification ability of the SVM was nearly equal to that of the neural network model.  ... 
doi:10.1016/s0929-6441(08)60005-3 fatcat:x6r54dxu25gc7c34qq3kebdid4

Machine Learning in Medical Imaging Before and After Introduction of Deep Learning

Kenji SUZUKI
2017 Medical Imaging and Information Sciences  
It started from an event in 2012 when a deep learning approach based on a convolutional neural network(CNN)won an overwhelming victory in the bestknown worldwide computer-vision competition, ImageNet Classification  ...  image data directly without object segmentation or feature extraction ; thus, it is the source of the power of deep learning.  ...  of the of clustered microcalcifications in digital mammograms : contrast of ribs in chest radiographs by means of massive applications of artificial neural networks, Med Phys, 19 training artificial neural  ... 
doi:10.11318/mii.34.14 fatcat:ui5aakxtknac3h2n6fak7chm6q

Medical image analysis based on deep learning approach

Muralikrishna Puttagunta, S. Ravi
2021 Multimedia tools and applications  
Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision.  ...  This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications.  ...  Fig. 1 a X-ray image with pulmonary masses [121] b CT image with lung nodule [82] c Digitized histo pathological tissue image [132] 2 Neural networks History of neural networks The study of artificial  ... 
doi:10.1007/s11042-021-10707-4 pmid:33841033 pmcid:PMC8023554 fatcat:cm522go4nbdbnglgzpw4nu7tbi

Artificial intelligence in breast ultrasound

Ge-Ge Wu, Li-Qiang Zhou, Jian-Wei Xu, Jia-Yu Wang, Qi Wei, You-Bin Deng, Xin-Wu Cui, Christoph F Dietrich
2019 World Journal of Radiology  
Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound.  ...  At last, we talk about the future perspectives of AI in breast ultrasound.  ...  CLASSIFICATION AND RECOGNITION According to the similarity of algorithm functions and forms, machine learning generally includes support vector machine, fuzzy logic, artificial neural network, etc., and  ... 
doi:10.4329/wjr.v11.i2.19 pmid:30858931 pmcid:PMC6403465 fatcat:lkn2m2h4nzdzbfvfdyam5r2czm

Medical Imaging using Deep Learning Models

Chetanpal Singh
2021 European Journal of Engineering and Technology Research  
However, there are two things that one needs to understand; that is, the implementation of Artificial Neural Networks and Convolutional Neural Networks as well as deep learning to know about medical image  ...  In clinical applications, medical imaging is one of the most important parameters as with the help of this; experts can detect, monitor, and diagnose any kind of problems that are there in the patient's  ...  in non-cancerous [37] breast TABLE VI : VI CNN USED IN THE MEDICAL ULTRASOUND MRI SERVICES Imaging Modality Methods Used Dataset Application Reference CT scan, ultrasound, and MRI cardiac 3D CNN reinforcement  ... 
doi:10.24018/ejers.2021.6.5.2491 fatcat:q523p745ujf6fgytx6nrswhwau

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

Deep Learning and Medical Diagnosis: A Review of Literature

Mihalj Bakator, Dragica Radosav
2018 Multimodal Technologies and Interaction  
A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted.  ...  The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis.  ...  Reference Method Data Source Application/Remarks [27] CNN Clinical images Classification of skin cancer; the results of the study were satisfactory, as the deep convolutional neural networks  ... 
doi:10.3390/mti2030047 fatcat:c6ulsgl3kndszedl7ewil6lotu

A review of the application of deep learning in medical image classification and segmentation

Lei Cai, Jingyang Gao, Di Zhao
2020 Annals of Translational Medicine  
of medical images and the work of our team in the field of big data analysis of medical imagec, especially the classification and segmentation of medical images.  ...  This review introduces the application of intelligent imaging and deep learning in the field of big data analysis and early diagnosis of diseases, combining the latest research progress of big data analysis  ...  Then we detailed the application of deep learning in the classification and segmentation of medical images, including fundus, CT/MRI tomography, ultrasound and digital pathology based on different imaging  ... 
doi:10.21037/atm.2020.02.44 pmid:32617333 pmcid:PMC7327346 fatcat:bywo4riijzemnlu6nilxzdumwu

Automated Segmentation of Abnormal Tissues in Medical Images

H Khastavaneh, H Ebrahimpour-komleh
2019 Journal of Biomedical Physics and Engineering  
Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions.  ...  Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients.  ...  One of these studies uses massive training artificial neural network (MTANN) technique for segmentation of MS lesions [27] .  ... 
doi:10.31661/jbpe.v0i0.958 pmid:34458189 pmcid:PMC8385212 fatcat:h2z2oykmsvhdnbffcqo5nhcm34
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