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Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review.

Waseem Rawat¹, Zenghui Wang²

¹Department of Electrical and Mining Engineering, University of South Africa, Florida 1710, South Africa wrawat10@gmail.com.

Neural Computation

|June 10, 2017

Summary

This summary is machine-generated.

Convolutional neural networks (CNNs) have resurged due to increased computing power and data. This review details their evolution and impact on image classification tasks.

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Convolutional Neural Networks (CNNs) have a history dating back to the 1980s but experienced a significant resurgence.
Advancements in computing power, availability of large labeled datasets, and improved algorithms fueled their recent progress.
The period since 2012 has been marked by rapid advancements in neural network research, with CNNs at the forefront.

Purpose of the Study:

To provide a comprehensive review of Convolutional Neural Networks (CNNs) specifically for image classification tasks.
To trace the development of CNNs from their origins to current state-of-the-art deep learning systems.
To analyze key factors contributing to the recent popularity and advancement of CNNs.

Main Methods:

A systematic review of over 300 publications related to CNNs and image classification.
Analysis of early successes, the role of CNNs in the deep learning renaissance, and influential symbolic works.
Examination of various improvement attempts, contributions, and challenges in the field.

Main Results:

CNNs have evolved significantly, moving from early applications to becoming central to modern deep learning systems.
Key developments include increased computational resources, vast labeled datasets, and algorithmic innovations.
The review identifies seminal works and analyzes numerous improvement strategies and their associated challenges.

Conclusions:

CNNs are a pivotal technology in modern image classification, driven by a confluence of hardware, data, and algorithmic progress.
Understanding the historical development and key contributions is crucial for appreciating the current state and future potential of CNNs.
Despite significant advancements, ongoing research continues to address remaining challenges and explore new trends in CNN applications.