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Detecting cervical spondylosis using deep learning

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.

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.

This study analyzes genetic alterations and expression patterns of protein kinases involved in phagocytosis across multiple cancers using TCGA data.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.

The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.

The authors visualized black holes and wormholes using code and ray-tracing programs.

Arrhythmias vary in type and treatment, and ECGs are used to detect them, though human interpretation can be inconsistent. The researchers tested four tree-based algorithms (gradient boosting, random forest, decision tree, and extra trees) on ECG data from over 10,000 patients.

Robot-assisted minimally invasive surgery (RMIS) benefits from increased precision and faster recovery, with force feedback from the surgical tool being critical for control. Researchers tested the use of neural networks for detecting the vanishing point of the tool, a key element for force feedback.