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

Subrato Bharati, Prajoy Podder, M. Rubaiyat Hossain Mondal
2020 arXiv   pre-print
This paper provides a systematic review of the literature on artificial neural network (ANN) based models for the diagnosis of breast cancer via mammography.  ...  The review also shows that the studies related to breast cancer detection applied different deep learning models to a number of publicly available datasets.  ...  as, naïve Bayes, random forest, support vector machine (SVM), decision tree, or by conducting artificial neural networks (ANN)-based approaches, deep neural networks (DNN) and spiking neural network (  ... 
arXiv:2006.01767v1 fatcat:jjy3d2mgabfrrnpbkbyskfb2pi

The NILS Study Protocol: A Retrospective Validation Study of an Artificial Neural Network Based Preoperative Decision-Making Tool for Noninvasive Lymph Node Staging in Women with Primary Breast Cancer (ISRCTN14341750)

Ida Skarping, Looket Dihge, Pär-Ola Bendahl, Linnea Huss, Julia Ellbrant, Mattias Ohlsson, Lisa Rydén
2022 Diagnostics  
The pilot noninvasive lymph node staging (NILS) artificial neural network (ANN) model to predict nodal status was published in 2019, showing the potential to identify patients with a low risk of SLN metastasis  ...  Newly diagnosed breast cancer (BC) patients with clinical T1–T2 N0 disease undergo sentinel-lymph-node (SLN) biopsy, although most of them have a benign SLN.  ...  The NILS prediction model, which is an artificial neural network (ANN) model, was developed using retrospectively collected variables (including patients, n = 800).  ... 
doi:10.3390/diagnostics12030582 pmid:35328135 pmcid:PMC8947586 fatcat:d6ynwlpimfenpny4blhk7kin2a

Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

Md Manjurul Ahsan, Shahana Akter Luna, Zahed Siddique
2022 Healthcare  
Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases.  ...  The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application  ...  To detect breast cancer, Bhattacherjee et al. (2020) employed a backpropagation neural network (BNN).  ... 
doi:10.3390/healthcare10030541 pmid:35327018 pmcid:PMC8950225 fatcat:5obhqlgyendtbgq4werl6ruxda

Application of Machine Learning to Stomatology: A Comprehensive Review

Mao-Lei Sun, Yun Liu, Guo-Min Liu, Dan Cui, Ali Asghar Heidari, Wen-Yuan Jia, Xuan Ji, Hui-Ling Chen, Yun-Gang Luo
2020 IEEE Access  
An SVM classifier based on a rough set (RS) was developed to diagnose breast cancer [94] .  ...  Verner, "Artificial neural networks in business: Two decades of research," Applied Soft Computing, vol. 38, pp. 788-804, Jan 2016. [37] A.  ...  He is also a reviewer for many journals such as Applied Soft Computing, Artificial Intelligence in Medicine, Knowledge-based Systems, Future Generation Computer System.  ... 
doi:10.1109/access.2020.3028600 fatcat:pimgs5g2wrecrmce6iyh2sgti4

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

Xiaomin Zhou, Chen Li, Md Mamunur Rahaman, Yudong Yao, Shiliang Ai, Changhao Sun, Qian Wang, Yong Zhang, Mo Li, Xiaoyan Li, Tao Jiang, Dan Xue (+2 others)
2020 IEEE Access  
In this review, we present a comprehensive overview of the BHIA techniques based on ANNs.  ...  To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast  ...  CONCLUSION AND FUTURE WORK In this review, the methods of breast cancer histopathological image analysis based on the artificial neural network are comprehensively summarized, which are grouped into the  ... 
doi:10.1109/access.2020.2993788 fatcat:33e54jp6hzggtozlngxvotcpsu

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks [article]

Xiaomin Zhou, Chen Li, Md Mamunur Rahaman, Yudong Yao, Shiliang Ai, Changhao Sun, Xiaoyan Li, Qian Wang, Tao Jiang
2020 arXiv   pre-print
In this review, we present a comprehensive overview of the BHIA techniques based on ANNs.  ...  To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast  ...  CONCLUSION AND FUTURE WORK In this review, the methods of breast cancer histopathological image analysis based on the artificial neural network are comprehensively summarized, which are grouped into the  ... 
arXiv:2003.12255v2 fatcat:dghl3hszhrb7zlidym5z2x3mvq

Machine learning in handling disease outbreaks: a comprehensive review

Dianadewi Riswantini, Ekasari Nugraheni
2022 Bulletin of Electrical Engineering and Informatics  
A huge amount of multimodal medical data was used by previous studies for prediction, forecasting, classification, or screening purposes to resolve many problems of diseases, including epidemiological  ...  During the period 2010-2019, logistic regression and random forest are widely applied in the research in addition to the artificial neural network.  ...  In the early period of 2000-2009, the Bayesian network and neural network were widely used in the research.  ... 
doi:10.11591/eei.v11i4.3612 fatcat:tmhzunn72fc3rpalf7tr3jgoz4

CA125 and Ovarian Cancer: A Comprehensive Review

Parsa Charkhchi, Cezary Cybulski, Jacek Gronwald, Fabian Oliver Wong, Steven A. Narod, Mohammad R. Akbari
2020 Cancers  
This review covers the role of CA125 in the diagnosis and management of ovarian cancer and explores novel and more effective screening strategies with CA125.  ...  The recent shift of focus to the diagnosis of low volume type II ovarian cancer in its early stages of evolution provides a new and valuable target for screening.  ...  Other groups have focused on more complex computational methods such as artificial neural networks (ANN).  ... 
doi:10.3390/cancers12123730 pmid:33322519 fatcat:gov53dsijjcd5abqy67jztc7j4

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

Song Li, Yu-Qin Deng, Zhi-Ling Zhu, Hong-Li Hua, Ze-Zhang Tao
2021 Diagnostics  
AI tools, especially radiomics and artificial neural network methods.  ...  In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC.  ...  Relationship between artificial intelligence, machine learning, neural network, and deep learning.  ... 
doi:10.3390/diagnostics11091523 pmid:34573865 pmcid:PMC8465998 fatcat:l6z7elk4sbeyvhii7m4p2wmf54

Investigation of Flash Floods on early basis: A Factual Comprehensive review

Talha Ahmed Khan, Muhammad Alam, Zeeshan Shahid, M.S Mazliham
2020 IEEE Access  
Ultimate extreme flash floods can be acknowledged as a main reason of high casualties and infrastructure loss in many countries like Pakistan,  ...  Almost 10-30% cancers are not detected by mammographic screening. Video pulse radars were used to identify the buried structure like pipes, cables and mines [37] . The breast was modeled with tumor.  ...  model (A and B) forward back propagation (type of neural network) were used.  ... 
doi:10.1109/access.2020.2967496 fatcat:4d4dfn64frhn5mhrnpv6mcfm5q

Machine Learning Techniques for Biomedical Natural Language Processing: A comprehensive Review

Essam H. Houssein, Rehab E. Mohamed, Abdelmgeid A. Ali
2021 IEEE Access  
Moreover, this review summarizes the utilizing of Deep Learning and Machine Learning techniques in biomedical NLP tasks based on chronic diseases related EHR data.  ...  Natural language processing (NLP) techniques have been used as an artificial intelligence strategy to extract information from clinical narratives in electronic health records since they include a great  ...  Many of the major deep learning algorithms and architectures are based on the artificial neural network (ANN) architecture.  ... 
doi:10.1109/access.2021.3119621 fatcat:pl7h35nvqngk3gxpbdxvrgzg2u

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review [article]

Xiaoqi Li, Haoyuan Chen, Chen Li, Md Mamunur Rahaman, Xintong Li, Jian Wu, Xiaoyan Li, Hongzan Sun, Marcin Grzegorzek
2022 arXiv   pre-print
This paper reviews the applications of image processing technology based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection.  ...  These ML methods include artificial neural networks (ANN), support vector machines (SVM), clustering, and so on.  ...  Motivation of this Review This paper comprehensively reviews the MV methods for lymphatic histopathology image analysis.  ... 
arXiv:2201.08550v2 fatcat:tzc4oiurzngkrlke4w4zt4f26u

Medical Image Registration Using Deep Neural Networks: A Comprehensive Review [article]

Hamid Reza Boveiri, Raouf Khayami, Reza Javidan, Ali Reza MehdiZadeh
2020 arXiv   pre-print
In this paper, a comprehensive review on the state-of-the-art literature known as medical image registration using deep neural networks is presented.  ...  On the other hand, the recently huge progress in the field of machine learning made by the possibility of implementing deep neural networks on the contemporary many-core GPUs opened up a promising window  ...  In this paper, a comprehensive systematic review on the medical image registration using deep neural networks is presented.  ... 
arXiv:2002.03401v1 fatcat:u4utrifr2rg3bf6x6fgohyfmpy

A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis [article]

Yixin Li, Chen Li, Xiaoyan Li, Kai Wang, Md Mamunur Rahaman, Changhao Sun, Hao Chen, Xinran Wu, Hong Zhang, Qian Wang
2021 arXiv   pre-print
In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models.  ...  Then, a thorough review of the recent research on the MRFs and CRFs of pathology images analysis is presented.  ...  Lastly, based on the pixel color intensity feature, an artificial neural network (ANN) is employed to obtain an intensity-based score of ER cells.  ... 
arXiv:2009.13721v3 fatcat:q46wb3rhwjcode3b46h6v2lhoa

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Ezzeddin Bakhtavar, Mahsa Valipour, Samuel Yousefi, Rehan Sadiq, Kasun Hewage
2020 Complex & Intelligent Systems  
After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out.  ...  This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020.  ...  Additionally, FMEA, Bayesian networks, FIS, and artificial neural networks are among other most useful methods used along with FCMs for systems risk analysis.  ... 
doi:10.1007/s40747-020-00228-2 fatcat:mwhtolog7bgpdkn2ffo7isstim
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