Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review

Latif, Jahanzaib; Xiao, Chuangbai; Imran, Azhar; Tu, Shanshan

doi:10.1109/icomet.2019.8673502

Cited by 125 publications

(86 citation statements)

References 21 publications

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“…Meanwhile, Insilico Medicine situated in Hong Kong as of late declared that it's AI calculations had planned six new atoms that could end viral replication [6]. Because medical information continues to improve rapidly, medical data has grown quickly and huge numbers of medical devices have created an incredible risk on current hospital information systems [7]. The field of healthcare generates a wide range of data on medical diagnosis, patient records, treatment, medication, diagnosis, etc.…”

Section: Introductionmentioning

confidence: 99%

Notice of Retraction: AI Techniques for COVID-19

Hussain¹,

et al. 2020

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Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.

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Section: Introductionmentioning

confidence: 99%

Notice of Retraction: AI Techniques for COVID-19

Hussain¹,

et al. 2020

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“…Deep Learning can extract new features automatically from raw data. Figure 9 shows this point clearly [13].…”

Section: Deep Learning Comparison With Conventional Machine Learnmentioning

confidence: 81%

Machine Learning and Deep Learning

Chahal¹,

Gulia²

2019

IJITEE

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 Abstract: Now-a-days artificial intelligence has become an asset for engineering and experimental studies, just like statistics and calculus. Data science is a growing field for researchers and artificial intelligence, machine learning and deep learning are roots of it. This paper describes the relation between these roots of data science. There is a need of machine learning if any kind of analysis is to be performed. This study describes machine learning from the scratch. It also focuses on Deep Learning. Deep learning can also be known as new trend of machine learning. This paper gives a light on basic architecture of Deep learning. A comparative study of machine learning and deep learning is also given in the paper and allows researcher to have a broad view on these techniques so that they can understand which one will be preferable solution for a particular problem.

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“…Moving forward to the beginning of 2019, research has expanded on medical image analysis using deep learning and that was clearly proved by the count of papers and surveys published this year. Latif et al [31] published a review on medical imaging using machine learning and deep learning algorithms. They provided an outline for researchers including existing medical imaging techniques with their advantages and drawbacks.…”

Section: Surveys On Deep Learning In Medical Images Analysismentioning

confidence: 99%

“…Yet, researchers do not mind imposing non-deep-learning methods into the analysis process to improve the performance of deep learning proposed approaches. Referring to the work of Latif et al [31], the ML workflow starts by feeding the medical image into the algorithm, segmenting, extracting features, selecting features and discarding noises, classifying, and finally detecting the targets and deciding on the diagnosis. According to the same reference, deep learning algorithms can categorize, classify and enumerate patterns of diseases from images upon processing.…”

Section: Contributions To Deep Learning Applications In Pulmonary Medmentioning

confidence: 99%

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Farhat

Sakr

Kilany

2020

Machine Vision and Applications

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Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

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Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review

Cited by 125 publications

References 21 publications

Notice of Retraction: AI Techniques for COVID-19

Notice of Retraction: AI Techniques for COVID-19

Machine Learning and Deep Learning

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

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