A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review

S Kumar, H Kumar, G Kumar, SP Singh, A Bijalwan… - BMC Medical …, 2024 - Springer
Background Lung diseases, both infectious and non-infectious, are the most prevalent
cause of mortality overall in the world. Medical research has identified pneumonia, lung …

Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey

A Kosar, M Asif, MB Ahmad, W Akram… - Artificial Intelligence in …, 2024 - Elsevier
The unpredictable pandemic came to light at the end of December 2019, known as the novel
coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The …

Multi-level training and testing of CNN models in diagnosing multi-center COVID-19 and pneumonia X-ray images

M Talaat, X Si, J Xi - Applied Sciences, 2023 - mdpi.com
Featured Application Despite their reported high accuracy, a significant limitation of current
AI-assisted COVID-19 diagnostic models is that they are often trained on datasets sourced …

Artificial neural network based prediction of the lung tissue involvement as an independent in‐hospital mortality and mechanical ventilation risk factor in COVID‐19

M Parczewski, J Kufel, B Aksak‐Wąs… - Journal of Medical …, 2023 - Wiley Online Library
Introduction During COVID‐19 pandemic, artificial neural network (ANN) systems have been
providing aid for clinical decisions. However, to achieve optimal results, these models …

Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T1 and T2 Relaxation Times with Application to Cancer Cell …

A Truszkiewicz, D Bartusik-Aebisher, Ł Wojtas… - International Journal of …, 2023 - mdpi.com
Artificial intelligence has been entering medical research. Today, manufacturers of
diagnostic instruments are including algorithms based on neural networks. Neural networks …

Predicting the negative conversion time of nonsevere COVID‐19 patients using machine learning methods

J Ye, X Shao, Y Yang, F Zhu - Journal of Medical Virology, 2023 - Wiley Online Library
Based on the patient's clinical characteristics and laboratory indicators, different machine‐
learning methods were used to develop models for predicting the negative conversion time …

[HTML][HTML] Classification and detection of Covid-19 based on X-Ray and CT images using deep learning and machine learning techniques: A bibliometric analysis

Y Chawki, K Elasnaoui, M Ouhda - AIMS Electronics and Electrical …, 2024 - aimspress.com
During the COVID-19 pandemic, it was crucial for the healthcare sector to detect and classify
the virus using X-ray and CT scans. This has underlined the need for advanced Deep …

Comparing Convolutional Neural Networks for Covid-19 Detection in Chest X-Ray Images

N Varshney, P Madan, A Shrivastava… - 2023 10th IEEE Uttar …, 2023 - ieeexplore.ieee.org
The study has been conducted to understand the effectiveness of CNN in the case of
“COVID-19” detection by using all the X-ray images of the chest. This helps the health sector …

Management of Misinformation in Critical Healthcare Using Machine Learning Algorithms and Models

W Nwankwo, K Ukaoha, AN Osika… - IUP Journal of …, 2023 - search.proquest.com
The volume of activities and information disseminated daily on the Internet through different
social media platforms and channels is increasing geometrically, and we can simply assert …

The Design of Convolutional Neural Networks Model for Classification of Ear Diseases on Android Mobile Devices

IGPS Wijaya, H Mulyana, H Kadriyan… - JOIV: International Journal …, 2023 - joiv.org
An otorhinolaryngologist (ORL) or general practitioner diagnoses ear disease based on ear
image information. However, general practitioners refer patients to ORL for chronic ear …