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The authors looked at different models of semantic segmentation to determine which may be best used in the future for segmentation of CT scans to help diagnose certain conditions.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

Semantic segmentation - labelling each pixel in an image to a specific class- models require large amounts of manually labeled and collected data to train.

In this study, the authors seek to improve a machine learning algorithm used for image classification: identifying male and female images. In addition to fine-tuning the classification model, they investigate how accuracy is affected by their changes (an important task when developing and updating algorithms). To determine accuracy, a set of images is used to train the model and then a separate set of images is used for validation. They found that the validation accuracy was close to the training accuracy. This study contributes to the expanding areas of machine learning and its applications to image identification.

Skin cancer is a common and potentially deadly form of cancer. This study’s purpose was to develop an automated approach for early detection for skin cancer. We hypothesized that convolutional neural network-based models using transfer learning could accurately differentiate between benign and malignant moles using natural images of human skin.

In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!

Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. Epidemiological models help propose pandemic control strategies based on non-pharmaceutical interventions such as social distancing, curfews, and lockdowns, reducing the economic impact of these restrictions. In this research, we utilized an epidemiological Susceptible, Exposed, Infected, Recovered, Deceased (SEIRD) model – a compartmental model for virtually simulating a pandemic day by day.

The authors used a particle-in-cell simulation to determine the effects on extensive and intensive metrics. They found that preliminary simulations could be run quickly with much lower particle counts before more technically demanding and comprehensive simulations are performed.

This study shows the efficacy of leveraging transfer learning, specifically from residual networks, to detect CVDs and possible signs of CVDs. The findings indicate that leveraging transfer learning from residual networks alongside medical professionals is a highly promising approach for CVD detection and diagnosis, warranting further investigation.

The authors used developed a machine learning tool for studying social media discourse surrounding use of tobacco and cannabis.