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Literature review: Efficient deep neural networks techniques for medical image analysis
MA Abdou - Neural Computing and Applications, 2022 - Springer
Significant evolution in deep learning took place in 2010, when software developers started
using graphical processing units for general-purpose applications. From that date, the deep …
using graphical processing units for general-purpose applications. From that date, the deep …
Deep learning for smart Healthcare—A survey on brain tumor detection from medical imaging
M Arabahmadi, R Farahbakhsh, J Rezazadeh - Sensors, 2022 - mdpi.com
Advances in technology have been able to affect all aspects of human life. For example, the
use of technology in medicine has made significant contributions to human society. In this …
use of technology in medicine has made significant contributions to human society. In this …
MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers
Brain tumor classification plays an important role in clinical diagnosis and effective
treatment. In this work, we propose a method for brain tumor classification using an …
treatment. In this work, we propose a method for brain tumor classification using an …
Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet
H Panwar, PK Gupta, MK Siddiqui… - Chaos, Solitons & …, 2020 - Elsevier
Presently, COVID-19 has posed a serious threat to researchers, scientists, health
professionals, and administrations around the globe from its detection to its treatment. The …
professionals, and administrations around the globe from its detection to its treatment. The …
A survey on deep learning in medicine: Why, how and when?
F Piccialli, V Di Somma, F Giampaolo, S Cuomo… - Information …, 2021 - Elsevier
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
Deep learning in medical image registration: a survey
The establishment of image correspondence through robust image registration is critical to
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …
many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring …
Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation
J Wen, E Thibeau-Sutre, M Diaz-Melo… - Medical image …, 2020 - Elsevier
Numerous machine learning (ML) approaches have been proposed for automatic
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 …
Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images
N Ghassemi, A Shoeibi, M Rouhani - Biomedical Signal Processing and …, 2020 - Elsevier
In this paper, a new deep learning method for tumor classification in MR images is
presented. A deep neural network is first pre-trained as a discriminator in a generative …
presented. A deep neural network is first pre-trained as a discriminator in a generative …
[HTML][HTML] A transfer learning approach for AI-based classification of brain tumors
R Mehrotra, MA Ansari, R Agrawal… - Machine Learning with …, 2020 - Elsevier
Abstract Classification of Brain Tumor (BT) is a vital assignment for assessing Tumors and
making a suitable treatment. There exist numerous imaging modalities that are utilized to …
making a suitable treatment. There exist numerous imaging modalities that are utilized to …
Explainable artificial intelligence models using real-world electronic health record data: a systematic sco** review
SN Payrovnaziri, Z Chen… - Journal of the …, 2020 - academic.oup.com
Objective To conduct a systematic sco** review of explainable artificial intelligence (XAI)
models that use real-world electronic health record data, categorize these techniques …
models that use real-world electronic health record data, categorize these techniques …