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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Toolkits and Libraries for Deep Learning.

Bradley J Erickson¹, Panagiotis Korfiatis², Zeynettin Akkus²

¹Mayo Clinic, 200 First St SW, Rochester, MN, 55905, USA. bje@Mayo.edu.

Journal of Digital Imaging

|March 19, 2017

Summary

This summary is machine-generated.

Deep learning, particularly convolutional neural networks (CNNs), offers advanced medical image analysis by automatically learning features. This paper reviews tools for implementing deep learning in medical imaging tasks like segmentation and classification.

Keywords:

Artificial intelligence Convolutional neural network Deep learning Machine learning

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Area of Science:

Artificial Intelligence
Machine Learning
Medical Imaging

Background:

Deep learning is a subset of machine learning utilizing neural networks with multiple layers.
Convolutional Neural Networks (CNNs) are a popular deep learning architecture for image analysis.
Traditional machine learning requires manual feature engineering, unlike deep learning.

Purpose of the Study:

To describe libraries and tools for deep learning in medical imaging.
To facilitate the construction and execution of deep learning models for medical image analysis.

Main Methods:

Review of existing deep learning libraries and tools.
Focus on applications in medical imaging tasks such as segmentation, detection, and classification.

Main Results:

Identification of key libraries and tools for deep learning in medical imaging.
Demonstration of deep learning's capability to automatically learn features for image analysis.

Conclusions:

Deep learning, especially CNNs, revolutionizes medical image analysis.
Availability of tools simplifies the implementation of advanced deep learning techniques in clinical practice.