An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review

S Das, GK Nayak, L Saba, M Kalra, JS Suri… - Computers in biology and …, 2022 - Elsevier
Background Artificial intelligence (AI) has become a prominent technique for medical
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 …

[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 …

Role of ensemble deep learning for brain tumor classification in multiple magnetic resonance imaging sequence data

GS Tandel, A Tiwari, OG Kakde, N Gupta, L Saba… - Diagnostics, 2023 - mdpi.com
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 …

[HTML][HTML] Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

I Shiri, M Sorouri, P Geramifar, M Nazari… - Computers in biology …, 2021 - Elsevier
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 …

Coronavirus disease (COVID-19) prevention and treatment methods and effective parameters: A systematic literature review

AM Rahmani, SYH Mirmahaleh - Sustainable cities and society, 2021 - Elsevier
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 …

[HTML][HTML] Segmentation-based classification deep learning model embedded with explainable AI for COVID-19 detection in chest X-ray scans

N Sharma, L Saba, NN Khanna, MK Kalra, MM Fouda… - Diagnostics, 2022 - mdpi.com
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 …

[HTML][HTML] Brain tumor characterization using radiogenomics in artificial intelligence framework

B Jena, S Saxena, GK Nayak, A Balestrieri, N Gupta… - Cancers, 2022 - mdpi.com
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 …

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A …

M Agarwal, S Agarwal, L Saba, GL Chabert… - Computers in biology …, 2022 - Elsevier
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 …

Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: a neuro-oncological investigation

S Saxena, B Jena, B Mohapatra, N Gupta… - Computers in Biology …, 2023 - Elsevier
Abstract Background The O6-methylguanine-DNA methyltransferase (MGMT) is a
deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential …