Automated detection and forecasting of covid-19 using deep learning techniques: A review
A Shoeibi, M Khodatars, M Jafari, N Ghassemi… - Neurocomputing, 2024 - Elsevier
Abstract In March 2020, the World Health Organization (WHO) declared COVID-19 a global
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …
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 …
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] 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 …
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 …
Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review
BM de Vries, GJC Zwezerijnen, GL Burchell… - Frontiers in …, 2023 - frontiersin.org
Rational Deep learning (DL) has demonstrated a remarkable performance in diagnostic
imaging for various diseases and modalities and therefore has a high potential to be used …
imaging for various diseases and modalities and therefore has a high potential to be used …
Attention-enabled ensemble deep learning models and their validation for depression detection: A domain adoption paradigm
Depression is increasingly prevalent, leading to higher suicide risk. Depression detection
and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo …
and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo …
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] A scoping review of interpretability and explainability concerning artificial intelligence methods in medical imaging
Abstract Purpose To review eXplainable Artificial Intelligence/(XAI) methods available for
medical imaging/(MI). Method A scoping review was conducted following the Joanna Briggs …
medical imaging/(MI). Method A scoping review was conducted following the Joanna Briggs …