The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment decisions. This rise has led to the development of Explainable AI (XAI) system to improve the transpare...
BACKGROUND AND OBJECTIVES: Severe pneumonia is the leading cause of death among young children worldwide, disproportionately impacting children who lack access to advanced diagnostic imaging. Here our objectives were to develop and test the accuracy ...
INTRODUCTION: Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects of diagnosis and planning can be further enhanced through the implementation of a quantum deep ne...
BACKGROUND: The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP pat...
Studies in health technology and informatics
39176895
This study explored machine learning's potential in predicting the nutritional status and outcomes for pneumonia patients. It focused on 4,368 patients in a Taiwan medical center from Jan 2016 to Feb 2022, excluding ICU cases. The average age was 77....
Journal of imaging informatics in medicine
39138748
Pneumonia is a severe health concern, particularly for vulnerable groups, needing early and correct classification for optimal treatment. This study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for pneumonia...
Pneumonia is a common postoperative complication in patients with aneurysmal subarachnoid hemorrhage (aSAH), which is associated with poor prognosis and increased mortality. The aim of this study was to develop a predictive model for postoperative pn...
Evaluating Community-Acquired Pneumonia (CAP) is crucial for determining appropriate treatment methods. In this study, we established a machine learning model using radiomics and clinical features to rapidly and accurately identify Severe Community-A...
BACKGROUND/AIM: In this study, we introduce an innovative deep-learning model architecture aimed at enhancing the accuracy of detecting and classifying organizing pneumonia (OP), a condition characterized by the presence of Masson bodies within the a...
This work proposes a novel technique called Enhanced JAYA (EJAYA) assisted Q-Learning for the classification of pulmonary diseases, such as pneumonia and tuberculosis (TB) sub-classes using chest x-ray images. The work introduces Fuzzy lattices forma...