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 …
Double AMIS-ensemble deep learning for skin cancer classification
This study aims to create a precise skin cancer classification system (SC-CS) able to
distinguish various skin cancer types. Targeted categories include melanoma, vascular …
distinguish various skin cancer types. Targeted categories include melanoma, vascular …
Weighted average ensemble deep learning model for stratification of brain tumor in MRI images
Brain tumor diagnosis at an early stage can improve the chances of successful treatment
and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures …
and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures …
XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease
F Yi, H Yang, D Chen, Y Qin, H Han, J Cui… - BMC medical informatics …, 2023 - Springer
Background Due to the class imbalance issue faced when Alzheimer's disease (AD)
develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical …
develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical …
[HTML][HTML] Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation
The COVID-19 pandemic continues to spread rapidly over the world and causes a
tremendous crisis in global human health and the economy. Its early detection and …
tremendous crisis in global human health and the economy. Its early detection and …
Class overlap handling methods in imbalanced domain: A comprehensive survey
Class overlap in imbalanced datasets is the most common challenging situation for
researchers in the fields of deep learning (DL) machine learning (ML), and big data (BD) …
researchers in the fields of deep learning (DL) machine learning (ML), and big data (BD) …
Feature selection of pre-trained shallow CNN using the QLESCA optimizer: COVID-19 detection as a case study
Abstract According to the World Health Organization, millions of infections and a lot of
deaths have been recorded worldwide since the emergence of the coronavirus disease …
deaths have been recorded worldwide since the emergence of the coronavirus disease …
The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance
C Kaope, Y Pristyanto - MATRIK: Jurnal …, 2023 - journal.universitasbumigora.ac.id
Class imbalance is a condition where the amount of data in the minority class is smaller than
that of the majority class. The impact of the class imbalance in the dataset is the occurrence …
that of the majority class. The impact of the class imbalance in the dataset is the occurrence …
Stroke Prediction Using Deep Learning and Transfer Learning Approaches
Stroke is one of the leading causes of death and disability worldwide. The ideal solution to
the stroke problem is to prevent it in advance by controlling metabolic factors, atrial …
the stroke problem is to prevent it in advance by controlling metabolic factors, atrial …
Genetic algorithm-based hybrid deep learning model for explainable Alzheimer's disease prediction using temporal multimodal cognitive data
H Saleh, N ElRashidy, M Abd Elaziz… - International Journal of …, 2024 - Springer
Alzheimer's disease (AD) is an irreversible neurodegenerative disease characterized by
progressive neuronal deterioration. Early detection of AD is critical for mitigating disease …
progressive neuronal deterioration. Early detection of AD is critical for mitigating disease …