A systematic review on deep structured learning for COVID-19 screening using chest CT from 2020 to 2022
KC Santosh, D GhoshRoy, S Nakarmi - Healthcare, 2023 - mdpi.com
The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel
coronavirus. The World Health Organization (WHO) designated it as a global pandemic on …
coronavirus. The World Health Organization (WHO) designated it as a global pandemic on …
Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey
MAA Al-qaness, J Zhu, D AL-Alimi, A Dahou… - … Methods in Engineering, 2024 - Springer
In medical imaging, the last decade has witnessed a remarkable increase in the availability
and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant …
and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant …
Explainable COVID-19 detection based on chest x-rays using an end-to-end RegNet architecture
M Chetoui, MA Akhloufi, EM Bouattane, J Abdulnour… - Viruses, 2023 - mdpi.com
COVID-19, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-
CoV-2), is one of the worst pandemics in recent history. The identification of patients …
CoV-2), is one of the worst pandemics in recent history. The identification of patients …
Grid-search integrated optimized support vector machine model for breast cancer detection
Breast cancer is a common and highly heterogeneous cancer worldwide. Rapid detection
and early diagnosis are essential in its treatment, but it is challenging due to mammogram's …
and early diagnosis are essential in its treatment, but it is challenging due to mammogram's …
Peer-to-peer federated learning for COVID-19 detection using transformers
M Chetoui, MA Akhloufi - Computers, 2023 - mdpi.com
The simultaneous advances in deep learning and the Internet of Things (IoT) have benefited
distributed deep learning paradigms. Federated learning is one of the most promising …
distributed deep learning paradigms. Federated learning is one of the most promising …
BrainSegNeT: a lightweight brain tumor segmentation model based on U-net and progressive neuron expansion
Brain tumor segmentation is a critical task in medical image analysis. In recent years,
several deep learning-based models have been developed for brain tumor segmentation …
several deep learning-based models have been developed for brain tumor segmentation …
A diagnosis model for brain atrophy using deep learning and MRI of type 2 diabetes mellitus
Objective Type 2 Diabetes Mellitus (T2DM) is linked to cognitive deterioration and
anatomical brain abnormalities like cerebral brain atrophy and cerebral diseases. We aim to …
anatomical brain abnormalities like cerebral brain atrophy and cerebral diseases. We aim to …
AI-enabled case detection model for infectious disease outbreaks in resource-limited settings
C Sisimayi, C Harley, F Nyabadza… - Frontiers in Applied …, 2023 - frontiersin.org
Introduction The utility of non-contact technologies for screening infectious diseases such as
COVID-19 can be enhanced by improving the underlying Artificial Intelligence (AI) models …
COVID-19 can be enhanced by improving the underlying Artificial Intelligence (AI) models …
[HTML][HTML] Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images
There are concerns that artificial intelligence (AI) algorithms may create underdiagnosis bias
by mislabeling patient individuals with certain attributes (eg, female and young) as healthy …
by mislabeling patient individuals with certain attributes (eg, female and young) as healthy …
A breast cancer detection model using a tuned svm classifier
Breast cancer has become a common disease that affects women all over the world. Early
detection and diagnosis of the breast cancer is crucial for an effective medication and …
detection and diagnosis of the breast cancer is crucial for an effective medication and …