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A Systematic Collection of Medical Image Datasets for Deep Learning
[article]
2021
arXiv
pre-print
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Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research. ...
The lack of data in the medical imaging field creates a bottleneck for the application of deep learning to medical image analysis. ...
Acknowledgments We thanks for the projects of National Natural Science Foundation of China (62072358), Zhejiang University special scientific research fund for COVID-19 preverntion and control, National ...
arXiv:2106.12864v1
fatcat:bjzkgce2xvaexmb6cdznws7fye
Medical Deep Learning – A systematic Meta-Review
[article]
2020
arXiv
pre-print
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With these surveys as foundation, the aim of this contribution is to provide a very first high-level, systematic meta-review of medical deep learning surveys. ...
Hence, a complete overview of the field of 'medical deep learning' is almost impossible to obtain and getting a full overview of medical sub-fields becomes increasingly more difficult. ...
[51] propose a systematic review on deep learning or machine learning-based methods that have been used for the automatic detection of pulmonary nodules using a common dataset, namely the Lung Image ...
arXiv:2010.14881v4
fatcat:56nrzawncnaopcpuzlzac5ceoy
DEEP LEARNING-BASED CANCER CLASSIFICATION FOR MICROARRAY DATA: A SYSTEMATIC REVIEW
2021
Zenodo
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Deep neural networks are robust techniques and recently used extensively for building cancer classification models from different types of data. ...
As a result, CNN considers the most common neural network architecture used in the medical field due to its robustness and high performance in cancer classification. ...
Conflict of interest The authors declare that they have no conflict of interest. ...
doi:10.5281/zenodo.6126510
fatcat:vmqa4zuoqrdsxdq7rsflgh362y
MRI Image Segmentation, Prediction and Diagnostic Accuracy: Deep Learning Framework and Machine Learning Techniques Analysis for Reducing the impact of Cardiac Diseases
2019
International Journal of Engineering and Advanced Technology
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Objectives: To assess the performance outcomes of various techniques for predicting the risk of cardiovascular diseases and MRI image segmentation method on the basis of systematic review. ...
Finally, we propose automatic myocardial segmentation method for cardiac MRI on the basis of Deep Convolutional neural network. ...
Deep learning technique has been successfully used as a tool for machine learning where a neural network is capable of automatically learning features. ...
doi:10.35940/ijeat.b3138.129219
fatcat:orgqhkyourf5hahzlche6g3vg4
Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities
2020
IEEE Access
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It provides 22,000 clinical images to a dermatologist for analysis purposes. c) MEDNODE dataset [32] : It consists of 100 melanoma and 70 naevus images which were collected from the University of Medical ...
Moreover, some articles made a self-collected image dataset using the internet.
1) Limited number of images in datasets Available benchmark datasets have a limited number of images for training and testing ...
doi:10.1109/access.2020.3001507
fatcat:hmlampsx3zetjphzqg4brun3xm
Applications of Artificial Intelligence and Machine Learning in Diagnosis and Prognosis of COVID-19 infection: A systematic review
2021
Frontiers in Health Informatics
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Neural networks and deep neural network variants were the most popular machine learning type. ...
The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. ...
CT images from one medical center collection in GitHub and in Italy NIH Chest X-ray Dataset Predict mortality of COVID-19 patients using Kaggle Novel Corona Virus 2019 Dataset Predict COVID-19 and using ...
doi:10.30699/fhi.v10i1.321
fatcat:lqo6ffcoynfhbeotdmfzunybne
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
2019
The Lancet Digital Health
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Deep learning offers considerable promise for medical diagnostics. ...
Studies comparing the diagnostic performance of deep learning models and health-care professionals based on medical imaging, for any disease, were included. ...
In this systematic review, we have sought to critically appraise the current state of diagnostic performance by deep learning algorithms for medical imaging compared with health-care professionals, considering ...
doi:10.1016/s2589-7500(19)30123-2
pmid:33323251
fatcat:essufwppd5gc3hlr2v5bzjraoy
Deep Learning Applications in Analyzing Ultrasound Images of Thyroid Nodules: Protocol for a Systematic Review
2020
Frontiers in Health Informatics
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Objective: with the best of our knowledge there is not any articles that actually provide a systematic review of deep learning application in analyzing ultrasound images of thyroid nodules and Hence, a ...
study a protocol was used for doing a systematic review on various deep learning applications in thyroid ultrasound such as feature selection, classification, localization, detection and segmentation. ...
There are a few studies that review the application of deep learning for medical diagnosis but not on ultrasound images [14, 15] .There are a number of studies which summarize the research of ultrasound ...
doi:10.30699/fhi.v9i1.220
fatcat:cq2q35cievednilghp3cln6tiu
Inconsistency in the use of the term "validation" in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging
2020
PLoS ONE
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The development of deep learning (DL) algorithms is a three-step process-training, tuning, and testing. ...
We investigated the extent of inconsistency in usage of the term "validation" in studies on the accuracy of DL algorithms in providing diagnosis from medical imaging. ...
learning algorithms (including both DL and non-DL machine learning) for providing diagnosis from medical imaging. ...
doi:10.1371/journal.pone.0238908
pmid:32915901
fatcat:lelvqfpu7ng5hbpbmci6kxpb5i
A Survey on the Role of Artificial Intelligence in Biobanking Studies
2022
Diagnostics
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The search terms included "biobanks", "AI", "machine learning", and "deep learning", as well as combinations such as "biobanks with AI", "deep learning in the biobanking field", and "recent advances in ...
In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. ...
Acknowledgments: We thank the financial assistance of the European Union POR MARCHE FESR 2014/2020-AXIS 1-OS 2-CTION 2.1-Support for the development of a collaborative research platform in the fields of ...
doi:10.3390/diagnostics12051179
pmid:35626333
pmcid:PMC9140088
fatcat:caphh2hetnduxbpgxqyd2jhqqm
A Comparison of Transfer Learning Performance versus Health Experts in Disease Diagnosis from Medical Imaging
2020
IEEE Access
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This article presents a systematic literature review (SLR) by conducting a comparison of a variety of transfer learning approaches with healthcare experts in diagnosing diseases from medical imaging. ...
It has been significantly used for diagnosis of diseases in medical imaging. ...
Until now, the deep learning has faced three major issues in disease diagnostic procedures. First, access to a large amount of well-curated and labeled medical image databases. ...
doi:10.1109/access.2020.3004766
fatcat:ynlcahzrxndv3pe4uzwbsywoje
Skin Cancer Detection: A Review Using Deep Learning Techniques
2021
International Journal of Environmental Research and Public Health
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This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. ...
The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. ...
Acknowledgments: The authors acknowledge support from the Deanship of Scientific Research, Najran University, Kingdom of Saudi Arabia.
Conflicts of Interest: Authors have no conflicts of interest. ...
doi:10.3390/ijerph18105479
pmid:34065430
pmcid:PMC8160886
fatcat:amoeo6q5jnemfdpgkryryh6o3m
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
2019
The Lancet Digital Health
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We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding-and no deep learning-expertise. ...
In the case of the deep learning model developed on a subset of the HAM10000 dataset, we did external validation using the Edinburgh Dermofit Library dataset. ...
in datasets used for deep learning. 39 Apart from the dermatology image set, all datasets contained duplicate images. ...
doi:10.1016/s2589-7500(19)30108-6
pmid:33323271
fatcat:l4y4gmsm5bh35kddwfkn2y3lqa
Best Paper Selection
2021
IMIA Yearbook of Medical Informatics
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Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis. https://www.sciencedirect.com/science/article/abs/pii/S1361841520301237? ...
Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network. https://www.giejournal.org/article/S0016-5107(20)30132-2/ ...
Their results suggest a careful curation of data for training deep learning algorithms for medical image analysis. ...
doi:10.1055/s-0041-1726527
fatcat:3mb5rh3hqfgrxoerwgrrcxka74
Machine learning models for image-based diagnosis and prognosis of COVID-19: A systematic review (Preprint)
2020
JMIR Medical Informatics
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Most articles used deep learning methods based on CNN networks which have been used widely as a classification algorithm The most frequently reported predictors of prognosis in patients with COVID-19 included ...
A systematic search of the PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases up to 24 May 2020 is performed. To conduct this study, PRISMA guidelines were followed. ...
for the generation of synthetic CXR images.+
Design a CNN-based Using COVID GAN for
augmentation of training dataset
1124
(932, 192)
Deep learning
Accuracy: 95
Sensitivity: 90
Specificity: ...
doi:10.2196/25181
pmid:33735095
fatcat:jdztginrrbap3bywkgm75bwt2m
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