Deep learning: systematic review, models, challenges, and research directions

T Talaei Khoei, H Ould Slimane… - Neural Computing and …, 2023 - Springer
The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …

Backbones-review: Feature extractor networks for deep learning and deep reinforcement learning approaches in computer vision

O Elharrouss, Y Akbari, N Almadeed… - Computer Science …, 2024 - Elsevier
To understand the real world using various types of data, Artificial Intelligence (AI) is the
most used technique nowadays. While finding the pattern within the analyzed data …

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 …

Machine learning applications for COVID-19 outbreak management

A Heidari, N Jafari Navimipour, M Unal… - Neural Computing and …, 2022 - Springer
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted
practically every area of human life. Several machine learning (ML) approaches are …

Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated …

G Celik - Applied Soft Computing, 2023 - Elsevier
Covid-19 has become a worldwide epidemic which has caused the death of millions in a
very short time. This disease, which is transmitted rapidly, has mutated and different …

GCDN-Net: Garbage classifier deep neural network for recyclable urban waste management

MM Hossen, A Ashraf, M Hasan, ME Majid… - Waste management, 2024 - Elsevier
The escalating waste volume due to urbanization and population growth has underscored
the need for advanced waste sorting and recycling methods to ensure sustainable waste …

COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization

A Hamza, M Attique Khan, SH Wang… - Frontiers in Public …, 2022 - frontiersin.org
The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a
result, it has disastrous consequences for people's lives, public health, and the global …

A multi-objective segmentation method for chest X-rays based on collaborative learning from multiple partially annotated datasets

H Wang, D Zhang, J Feng, L Cascone, M Nappi… - Information Fusion, 2024 - Elsevier
Accurate segmentation of multiple targets, such as ribs, clavicles, heart, and lung fields, from
chest X-ray images is crucial for diagnosing various lung diseases. Currently, mainstream …

A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron

A Khan, SH Khan, M Saif, A Batool… - … of Experimental & …, 2024 - Taylor & Francis
ABSTRACT The Coronavirus (COVID-19) outbreak in December 2019 has drastically
affected humans worldwide, creating a health crisis that has infected millions of lives and …

RADIC: A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

O Attallah - Chemometrics and Intelligent Laboratory Systems, 2023 - Elsevier
Deep learning (DL) algorithms have demonstrated a high ability to perform speedy and
accurate COVID-19 diagnosis utilizing computed tomography (CT) and X-Ray scans. The …