Deep Convolutional Neural Network (CNN) for Large-Scale Images Classification

12 Pages Posted: 6 Nov 2019

See all articles by Hossein Eghbali

Hossein Eghbali

University of Eyvanekey

Najmeh Hajihosseini

university of Evanekey

Date Written: October 27, 2019


In the field of machine vision and image classification, many models and methods have been introduced, but explicitly, different algorithms and models of neural network based researches have acquired a great importance among image classification models. beside the applications of this science in identifying patterns, image processing, artificial intelligence, and robot control, on the other hand the different influence aspects in daily and real life is indispensable to every point of view is such as agricultural domains, weather forecasts, medical sciences, engineering and so on. The accuracy and the executive algorithm path are very important in recognition and classification result. The main objective of these architectures is to provide a model similar to the internal system of the human brain to analyze various systems based on experiences, here-at the final goal of these algorithms is the possibility to create the training flow in artificial networks, in order to provide deep learning so that the network can diagnosis like human brain. This is another aspect of the architectures and the subject of the algorithm implementation accuracy as the model ability for recognize images and act like human brain?

Keywords: Classification, Diagnosis, Images, Neural Network, Convolutional, ConvNet, Deep Neural Network, CNN, Artificial Intelligence

JEL Classification: C63

Suggested Citation

Eghbali, Hossein and Hajihosseini, Najmeh, Deep Convolutional Neural Network (CNN) for Large-Scale Images Classification (October 27, 2019). Available at SSRN: or

Hossein Eghbali (Contact Author)

University of Eyvanekey ( email )
Eyvanekey, semnan
+982334521563 (Phone)
+982334521562 (Fax)


Najmeh Hajihosseini

university of Evanekey ( email )


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