Image classification using supervised convolutional neural network

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Abstract

Deep learning algorithms, in particular Convolutional Neural Networks have made notable accomplishments in many large-scale image classification tasks in the past decade. In this paper, image classification is performed using Supervised Convolutional Neural Network (SCNN). In supervised learning model, algorithm learns on a labeled dataset. SCNN architecture is built with 15 layers viz, input layer, 9 middle layers and 5 final layers. Two datasets of different sizes are tested on SCNN framework on single CPU. With CIFAR10 dataset of 60000 images the network yielded an accuracy of 73% taking high processing time, while for 3000 images taken from MIO-TCD dataset resulted 96% accuracy with less computational time.

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CITATION STYLE

APA

Sravya, S. S., Krishna, K. S. R., & Suhasini, P. S. (2019). Image classification using supervised convolutional neural network. International Journal of Recent Technology and Engineering, 8(2), 4505–4507. https://doi.org/10.35940/ijrte.B3486.078219

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