[HTML][HTML] Challenges and limitations in applying radiomics to PET imaging: possible opportunities and avenues for research
A Stefano - Computers in Biology and Medicine, 2024 - Elsevier
Radiomics, the high-throughput extraction of quantitative imaging features from medical
images, holds immense potential for advancing precision medicine in oncology and beyond …
images, holds immense potential for advancing precision medicine in oncology and beyond …
Recent trend in medical imaging modalities and their applications in disease diagnosis: a review
Medical Imaging (MI) plays a crucial role in healthcare, including disease diagnosis,
treatment, and continuous monitoring. The integration of non-invasive techniques such as X …
treatment, and continuous monitoring. The integration of non-invasive techniques such as X …
Simultaneous super-resolution and classification of lung disease scans
Acute lower respiratory infection is a leading cause of death in developing countries. Hence,
progress has been made for early detection and treatment. There is still a need for improved …
progress has been made for early detection and treatment. There is still a need for improved …
A comprehensive review of machine learning used to combat COVID-19
Coronavirus disease (COVID-19) has had a significant impact on global health since the
start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed …
start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed …
Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study
To derive and validate an effective machine learning and radiomics‐based model to
differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric …
differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric …
Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning
Chest X-rays (CXR) are the most commonly used imaging methodology in radiology to
diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent …
diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent …
Human Centered Decision‐Making for COVID‐19 Testing Center Location Selection: Tamil Nadu—A Case Study
This paper proposes a blend of three techniques to select COVID‐19 testing centers. The
objective of the paper is to identify a suitable location to establish new COVID‐19 testing …
objective of the paper is to identify a suitable location to establish new COVID‐19 testing …
[HTML][HTML] Inf-Seg: Automatic segmentation and quantification method for CT-based COVID-19 diagnosis
S Faridoddin, O Mahdi - Информатика, телекоммуникации и …, 2022 - cyberleninka.ru
The global spread of the COVID-19 has increased the need for physicians and accurate and
efficient diagnostic tools. The best way to control the spread of COVID-19 is through public …
efficient diagnostic tools. The best way to control the spread of COVID-19 is through public …
Diagnosis of Pulmonary Edema and Covid-19 from CT slices using squirrel search algorithm, support vector machine and back propagation neural network
R Betshrine Rachel, KH Nehemiah… - Journal of Intelligent …, 2023 - content.iospress.com
Abstract A Computer Aided Diagnosis (CAD) framework to diagnose Pulmonary Edema (PE)
and covid-19 from the chest Computed Tomography (CT) slices were developed and …
and covid-19 from the chest Computed Tomography (CT) slices were developed and …
Personalized Chemotherapy Selection for Lung Cancer Patients Using Machine Learning and Computed Tomography
M Skalunova, F Shariaty, S Rozov… - 2023 International …, 2023 - ieeexplore.ieee.org
The treatment of lung cancer remains a significant challenge in modern medicine, with no
universally optimal treatment regimen established to date. In this study, we focused on …
universally optimal treatment regimen established to date. In this study, we focused on …