AI Medical Compendium Topic

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X-Rays

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Classification of Watermelon Seeds Using Morphological Patterns of X-ray Imaging: A Comparison of Conventional Machine Learning and Deep Learning.

Sensors (Basel, Switzerland)
In this study, conventional machine learning and deep leaning approaches were evaluated using X-ray imaging techniques for investigating the internal parameters (endosperm and air space) of three cultivars of watermelon seed. In the conventional mach...

Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation.

European radiology
OBJECTIVE: The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we ...

Deep Mining External Imperfect Data for Chest X-Ray Disease Screening.

IEEE transactions on medical imaging
Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large distribution. Therefore, it is substantial to integrate knowledge from mu...

Targeted transfer learning to improve performance in small medical physics datasets.

Medical physics
PURPOSE: To perform an in-depth evaluation of current state of the art techniques in training neural networks to identify appropriate approaches in small datasets.

Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach.

European journal of radiology
PURPOSE: Suspected fractures are among the most common reasons for patients to visit emergency departments and often can be difficult to detect and analyze them on film scans. Therefore, we aimed to design a Deep Learning-based tool able to help doct...

Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays.

Physical and engineering sciences in medicine
Covid-19 first occurred in Wuhan, China in December 2019. Subsequently, the virus spread throughout the world and as of June 2020 the total number of confirmed cases are above 4.7 million with over 315,000 deaths. Machine learning algorithms built on...

Bone age assessment based on deep convolution neural network incorporated with segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: Bone age assessment is not only an important means of assessing maturity of adolescents, but also plays an indispensable role in the fields of orthodontics, kinematics, pediatrics, forensic science, etc. Most studies, however, do not take in...

Assessing and mitigating the effects of class imbalance in machine learning with application to X-ray imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Machine learning (ML) algorithms are well known to exhibit variations in prediction accuracy when provided with imbalanced training sets typically seen in medical imaging (MI) due to the imbalanced ratio of pathological and normal cases. Thi...

COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.

Interdisciplinary sciences, computational life sciences
The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a major pandemic outbreak recently. Various diagnostic technologies have been under active development. The novel coronavirus disease (COVID-19) may induce ...