AIMC Topic: X-Rays

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Domain Adaptation and Feature Fusion for the Detection of Abnormalities in X-Ray Forearm Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The main challenge in adopting deep learning models is limited data for training, which can lead to poor generalization and a high risk of overfitting, particularly when detecting forearm abnormalities in X-ray images. Transfer learning from ImageNet...

The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
We provide an overview of the latest evidence on computer-aided detection (CAD) software for automated interpretation of chest radiographs (CXRs) for TB detection. CAD is a useful tool that can assist in rapid and consistent CXR interpretation for TB...

A deep learning-based recognition for dangerous objects imaged in X-ray security inspection device.

Journal of X-ray science and technology
Several limitations in algorithms and datasets in the field of X-ray security inspection result in the low accuracy of X-ray image inspection. In the literature, there have been rare studies proposed and datasets prepared for the topic of dangerous o...

A Deep Learning Model for Diagnosing COVID-19 and Pneumonia through X-ray.

Current medical imaging
BACKGROUND: The new global pandemic caused by the 2019 novel coronavirus (COVID-19), novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily life, public health security, and the global economy. Early detect...

Addressing the Intra-class Mode Collapse Problem using Adaptive Input Image Normalization in GAN-based X-ray Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to ge...

Wasserstein GAN based Chest X-Ray Dataset Augmentation for Deep Learning Models: COVID-19 Detection Use-Case.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The novel coronavirus infection (COVID-19) is still continuing to be a concern for the entire globe. Since early detection of COVID-19 is of particular importance, there have been multiple research efforts to supplement the current standard RT-PCR te...

Correlation Between Quality Evaluation Metrics and Teeth Detection Results in Panoramic X-Rays Using Deep Learning.

Studies in health technology and informatics
Panoramic images are one of the most requested exams by dentists for allowing the visualization of the entire mouth. Interpreting X-ray images is a time-consuming task in which misdiagnoses can occur due to the inexperience or fatigue of professional...

The Pitfalls of Using Open Data to Develop Deep Learning Solutions for COVID-19 Detection in Chest X-Rays.

Studies in health technology and informatics
Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community relies heavily on public data comprising numerous data sources. Model...