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Deep Learning and Medical Diagnosis: A Review of Literature

Mihalj Bakator, Dragica Radosav
2018 Multimodal Technologies and Interaction  
In this review the application of deep learning for medical diagnosis is addressed.  ...  A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted.  ...  This review paper provides a concise and simple approach to deep learning applications in medical diagnosis, and it can moderately contribute to the existing body of literature.  ... 
doi:10.3390/mti2030047 fatcat:c6ulsgl3kndszedl7ewil6lotu

A Review of Deep learning in Medical Diagnosis

Rachna Kumari
2020 International Journal for Research in Applied Science and Engineering Technology  
Medical diagnosis is a process of determining which disease a person's symptoms and signs indicate.  ...  This paper described deep learning technique use in medical diagnosis field. I.  ...  From the literature review, it is observed that there is huge scope of work in the field of medical diagnosis with the help of deep learning techniques.  ... 
doi:10.22214/ijraset.2020.32302 fatcat:ggtty6j6zvfr3pr2uwd6beuyla

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.  ...  With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled.  ...  Why graph-based deep learning for medical diagnosis and analysis?  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.  ...  With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Automatic Diagnosis of Pneumothorax from Chest Radiographs: A Systematic Literature Review [article]

Tahira Iqbal, Arslan Shaukat, Usman Akram, Zartasha Mustansar
2021 arXiv   pre-print
Among various medical imaging tools, chest radiographs are the most important and widely used diagnostic tool for detection of thoracic pathologies.  ...  Artificial Intelligence techniques especially deep learning methodologies have found to be giving promising results in automating the field of medicine.  ...  Conflict of Interest The authors have no conflict of interest to disclose. Funding No funding was received for this study.  ... 
arXiv:2012.11214v2 fatcat:c2oa374cvrgvteysabzokvn3ba

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Tahira Iqbal, Arslan Shaukat, Muhammad Usman Akram, Zartasha Mustansar, Aimal Khan
2021 IEEE Access  
Undoubtedly, several models are available for automatic diagnosis of pneumothorax, however a summarized review of the existing literature is still missing.  ...  So far, best results have been achieved by deep-learning based models with Area-underreceiver-operating-characteristic-curve (AUC) of 88.87% for classification, and Dice-similarity-coefficient (DSC) of  ...  In [107] a framework evolved from deep learning techniques was proposed for diagnosis of pneumothorax.  ... 
doi:10.1109/access.2021.3122998 fatcat:67uv5nnle5hf7c2yjaypal57vi

ECG Paper Record Digitization and Diagnosis Using Deep Learning

Siddharth Mishra, Gaurav Khatwani, Rupali Patil, Darshan Sapariya, Vruddhi Shah, Darsh Parmar, Sharath Dinesh, Prathamesh Daphal, Ninad Mehendale
2021 Journal of Medical and Biological Engineering  
The proposed work aims to convert ECG paper records into a 1-D signal and generate an accurate diagnosis of heart-related problems using deep learning.  ...  The accuracy of deep learning-based binarization is 97%. Further deep learning-based diagnosis approach of such digitized paper ECG records was having an accuracy of 94.4%.  ...  Declarations Conflict of interest The authors declare that they have no conflict of interest.  ... 
doi:10.1007/s40846-021-00632-0 pmid:34149335 pmcid:PMC8204064 fatcat:qlas24vxm5dbvn2xmbk6uuiu34

Deep learning in medical imaging and radiation therapy

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger
2018 Medical Physics (Lancaster)  
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies  ...  We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods  ...  Deep learning, history, and techniques Deep learning is a subfield of machine learning, which in turn is a field within AI.  ... 
doi:10.1002/mp.13264 pmid:30367497 fatcat:bottst5mvrbkfedbuocbrstcnm

Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning [article]

Mohammad Eslami, Solale Tabarestani, Ehsan Adeli, Glyn Elwyn, Tobias Elze, Mengyu Wang, Nazlee Zebardast, Nassir Navab, Malek Adjouadi
2022 arXiv   pre-print
With the advent of sophisticated machine learning (ML) techniques and the promising results they yield, especially in medical applications, where they have been investigated for different tasks to enhance  ...  Since visualization is such an effective tool for human comprehension, memorization, and judgment, we have presented a first-of-its-kind estimation approach we refer to as Visualized Learning for Machine  ...  review of the baseline diagnosis and of the input measures used when making that diagnosis.  ... 
arXiv:2205.04599v1 fatcat:ixsnh4lnlbbbbcte7iay3q3qba

Lung cancer diagnosis using deep attention based multiple instance learning and radiomics

Junhua Chen, Haiyan Zeng, Chong Zhang, Zhenwei Shi, Andre Dekker, Leonard Wee, Inigo Bermejo
2022 Medical Physics (Lancaster)  
In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem, which better reflects the diagnosis process in the clinical setting and provides higher interpretability of  ...  We selected radiomics as the source of input features and deep attention-based MIL as the classification algorithm.  ...  For automated diagnosis of lung cancer, a deep learning-based system can be applied in two levels: at nodule level, to identify potential malignant nodule(s) for further biopsy and performing diagnosis  ... 
doi:10.1002/mp.15539 pmid:35187667 pmcid:PMC9310706 fatcat:jvxmbff3gnflhmscg6hdcml2qi

Introduction to machine and deep learning for medical physicists

Sunan Cui, Huan‐Hsin Tseng, Julia Pakela, Randall K. Ten Haken, Issam El Naqa
2020 Medical Physics (Lancaster)  
Recent years have witnessed tremendous growth in the application of machine learning (ML) and deep learning (DL) techniques in medical physics.  ...  Here, a review of the basic aspects involved in ML/DL model building, including data processing, model training, and validation for medical physics applications is presented and discussed.  ...  ACKNOWLEDGMENTS This work was supported in part by the National Institutes of  ... 
doi:10.1002/mp.14140 pmid:32418339 fatcat:b6jc2fta6zdp7pv7bjqw2st6zm

Synergizing medical imaging and radiotherapy with deep learning

Hongming Shan, Xun Jia, Pingkun Yan, Yunyao Li, Harald Paganetti, Ge Wang
2020 Machine Learning: Science and Technology  
It is believed that deep learning in particular, and artificial intelligence and machine learning in general, will have a revolutionary potential to advance and synergize medical imaging and radiotherapy  ...  This article reviews deep learning methods for medical imaging (focusing on image reconstruction, segmentation, registration, and radiomics) and radiotherapy (ranging from planning and verification to  ...  Acknowledgment This work was partially support by NIH/NCI under award numbers R01CA233888, R01CA237267, R01CA227289, R37CA214639, and R01CA237269, and NIH/NIBIB under award number R01EB026646.  ... 
doi:10.1088/2632-2153/ab869f fatcat:aibfmfelcngkrk4ilwcs25c77a

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning

Fan Yang, Zhi-Ri Tang, Jing Chen, Min Tang, Shengchun Wang, Wanyin Qi, Chong Yao, Yuanyuan Yu, Yinan Guo, Zekuan Yu
2021 BMC Medical Imaging  
Purpose The objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms.  ...  Conclusion The successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed  ...  on deep learning.  ... 
doi:10.1186/s12880-021-00723-z pmid:34879818 pmcid:PMC8653800 fatcat:rsazyxzycze65hqpobutxapwda

A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-ray Medical Images

Channabasava Chola, Pramodha Mallikarjuna, Abdullah Y. Muaad, J. V. Bibal Benifa, Jayappa Hanumanthappa, Mugahed A. Al-antari
2021 Computer Sciences & Mathematics Forum  
The deep learning model of EfficientNetB0 consistently performed a better classification result, achieving overall diagnosis accuracies of 99.36% and 99.23% using CXR and CT images, respectively.  ...  The public datasets, consisting of 7863 and 2613 chest X-ray and CT images, are respectively used to deploy, train, and evaluate the proposed deep learning models.  ...  The rest of this paper is organized as follows: A review of the relevant literature is presented in Section 2; The technical aspects of the deep learning methods for classification systems are detailed  ... 
doi:10.3390/ioca2021-10909 fatcat:y7x6ecnomvgerjdmpqcxityhp4

Medical Quality Assessment and Professionalized Recommendations Based on Deep Learning

Qiqi Chen, Xingwei Liu, Mingyang Liao, Yi He, Feng Mu
2020 ICIC Express Letters  
In this paper, we develop a novel medical quality assessment and healthcare recommendation algorithm which is based on deep learning.  ...  or a department's specialty and deal with temporal changes of it, and a deep learning approach has been successfully employed in recommender systems to improve accuracy on recommendation.  ...  The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.  ... 
doi:10.24507/icicel.14.04.369 fatcat:6447ix66kncxnf3ljugvhoc3li
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