Machine learning–based classification systems have shown promise in detecting cancer and other diseases early, and a study published in Gynecologic Oncology found the same may hold true for ...

The researchers were able to fine-tune the model on the ImageNet-1K image classification dataset with 1% of the training data, using only 12 to 13 images per class.

There are many machine learning techniques for binary classification. One of the most powerful techniques is to use the LightGBM (lightweight gradient boosting machine) system. LightGBM is a ...

Specifically, the researchers' machine learning approach, called an actor-model framework, was especially good at finding a "sweet spot" for image contrast. Ghezzi uses Photoshop as an example.