Abstract: In certain specialized domain scenarios, the collection of image data is rendered time-consuming and costly due to a variety of unique issues, making the assembly of extensive image datasets ...

No system was recommended for individual prognostication, and the group considered that more detail in ulcer characterization was needed and that machine learning (ML)–based models may be the solution ...

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...

A deep learning project implementing a ResNet-based Convolutional Neural Network for classifying food images from the Food-101 dataset. This project demonstrates state-of-the-art computer vision ...

Division of Applied Chemistry, Faculty of Engineering, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan ...

Welcome to the Zero to Mastery Learn PyTorch for Deep Learning course, the second best place to learn PyTorch on the internet (the first being the PyTorch documentation). 00 - PyTorch Fundamentals ...

You will be redirected to our submission process. The “Machine Learning for Medical Image Analysis” Research Topic is dedicated to presentations from the 29th Conference in Medical Image Understanding ...

Globally, the growth of plastic production has increased exponentially from 1.5 million metric tons (Mt) in 1950 to 400.3 Mt in 2022, resulting in a substantial increase of marine litter along the ...

Abstract: Image classification is one of the central research in machine learning that has wide application in many fields. Many factors can adversely impact the classification accuracy, including ...

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