• Siddhant Dani
• P. S. Hanwate
• Hrishikesh Panse
• Kshitij Chaudhari
• Shruti Kotwal

With an increase in the speed of data generation , the widespread use of cameras for automation and surveillance, and the need for visual feedback for artificially intelligent devices around the world, the mass of image data being produced today has increased rapidly. The need for efficient image processing has risen to simplify the image related tasks. Categorical Image Classification requires thousands of images to train the system. Also the system requires a large amount of time to extract the features and for classification as well. Hence, for image recognition tasks, deep convolutional neural networks have been introduced and have obtained promising results in recent years. They are being widely deployed to analyze, detect and classify images for a diverse range of tasks. This paper offers a summary of existing systems designed for image classification using CNN. At first, it defines the terms Image Classification and CNN and reviews current surveys dealing with CNN implementations for Image Classification. Then, included a general overview of What is Image Classification?, and CNN architecture. Moreover, This paper provides a taxonomy of state-of-the-art methods for efficient and accurate CNN applications in relation to the Image Classification problem. Based on the present study, we stress the open barriers of research and potential future work directions in CNN based Image Classification systems.

Image Classification, CNN, Deep Learning, Image Processing, Deep Networks, Neural Networks.

"Survey on the use of CNN and Deep Learning in Image Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 1, page no.609-618, January-2021, Available :http://www.jetir.org/papers/JETIR2101084.pdf

"Survey on the use of CNN and Deep Learning in Image Classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 1, page no. pp609-618, January-2021, Available at : http://www.jetir.org/papers/JETIR2101084.pdf