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An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review
Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
[HTML][HTML] Increased risk of COVID-19 in patients with diabetes mellitus—current challenges in pathophysiology, treatment and prevention
T Gęca, K Wojtowicz, P Guzik, T Gora - International journal of …, 2022 - mdpi.com
Coronavirus disease—COVID-19 (coronavirus disease 2019) has become the cause of the
global pandemic in the last three years. Its etiological factor is SARS-CoV-2 (Severe Acute …
global pandemic in the last three years. Its etiological factor is SARS-CoV-2 (Severe Acute …
[HTML][HTML] Economics of artificial intelligence in healthcare: diagnosis vs. treatment
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 …
Role of ensemble deep learning for brain tumor classification in multiple magnetic resonance imaging sequence data
The biopsy is a gold standard method for tumor grading. However, due to its invasive nature,
it has sometimes proved fatal for brain tumor patients. As a result, a non-invasive computer …
it has sometimes proved fatal for brain tumor patients. As a result, a non-invasive computer …
[HTML][HTML] Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients
Objective To develop prognostic models for survival (alive or deceased status) prediction of
COVID-19 patients using clinical data (demographics and history, laboratory tests, visual …
COVID-19 patients using clinical data (demographics and history, laboratory tests, visual …
Coronavirus disease (COVID-19) prevention and treatment methods and effective parameters: A systematic literature review
Background and objective The coronavirus disease 2019 (COVID-19) outbreak was first
identified in Wuhan in December 2019, which was declared a pandemic virus by the world …
identified in Wuhan in December 2019, which was declared a pandemic virus by the world …
[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] Brain tumor characterization using radiogenomics in artificial intelligence framework
Simple Summary Radiogenomics is a relatively new advancement in the understanding of
the biology and behaviour of cancer in response to conventional treatments. One of the most …
the biology and behaviour of cancer in response to conventional treatments. One of the most …
Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A …
Abstract Background COVLIAS 1.0: an automated lung segmentation was designed for
COVID-19 diagnosis. It has issues related to storage space and speed. This study shows …
COVID-19 diagnosis. It has issues related to storage space and speed. This study shows …
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 …