[HTML][HTML] A systematic review on deep structured learning for COVID-19 screening using chest CT from 2020 to 2022
KC Santosh, D GhoshRoy, S Nakarmi - Healthcare, 2023 - mdpi.com
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel
coronavirus. The World Health Organization (WHO) designated it as a global pandemic on …
coronavirus. The World Health Organization (WHO) designated it as a global pandemic on …
[HTML][HTML] A survey on AI techniques for thoracic diseases diagnosis using medical images
Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib
cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis …
cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis …
[HTML][HTML] Segmentation-based classification deep learning model embedded with explainable AI for COVID-19 detection in chest X-ray scans
Background and Motivation: COVID-19 has resulted in a massive loss of life during the last
two years. The current imaging-based diagnostic methods for COVID-19 detection in …
two years. The current imaging-based diagnostic methods for COVID-19 detection in …
[HTML][HTML] Economics of artificial intelligence in healthcare: diagnosis vs. treatment
NN Khanna, MA Maindarkar, V Viswanathan… - Healthcare, 2022 - mdpi.com
Motivation: The price of medical treatment continues to rise due to (i) an increasing
population;(ii) an aging human growth;(iii) disease prevalence;(iv) a rise in the frequency of …
population;(ii) an aging human growth;(iii) disease prevalence;(iv) a rise in the frequency of …
[HTML][HTML] Ensemble deep learning derived from transfer learning for classification of COVID-19 patients on hybrid deep-learning-based lung segmentation: a data …
AK Dubey, GL Chabert, A Carriero, A Pasche… - Diagnostics, 2023 - mdpi.com
Background and motivation: Lung computed tomography (CT) techniques are high-
resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease …
resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease …
Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: a neuro-oncological investigation
Abstract Background The O6-methylguanine-DNA methyltransferase (MGMT) is a
deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential …
deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential …
[HTML][HTML] Attention-based UNet deep learning model for plaque segmentation in carotid ultrasound for stroke risk stratification: an artificial intelligence paradigm
Stroke and cardiovascular diseases (CVD) significantly affect the world population. The
early detection of such events may prevent the burden of death and costly surgery …
early detection of such events may prevent the burden of death and costly surgery …
UNet deep learning architecture for segmentation of vascular and non-vascular images: a microscopic look at UNet components buffered with pruning, explainable …
Biomedical image segmentation (BIS) task is challenging due to the variations in organ
types, position, shape, size, scale, orientation, and image contrast. Conventional methods …
types, position, shape, size, scale, orientation, and image contrast. Conventional methods …
[HTML][HTML] Deep learning paradigm for cardiovascular disease/stroke risk stratification in Parkinson's disease affected by COVID-19: a narrative review
Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-
curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to …
curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to …
[HTML][HTML] Recommender system for the efficient treatment of COVID-19 using a convolutional neural network model and image similarity
Background: Hospitals face a significant problem meeting patients' medical needs during
epidemics, especially when the number of patients increases rapidly, as seen during the …
epidemics, especially when the number of patients increases rapidly, as seen during the …