Open AccessImage Classification using Supervised Convolutional Neural Network
Author(s) -
Saripalli Sri Sravya,
K. Sri Rama Krishna,
Pallikonda Sarah Suhasini
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3486.078219
Subject(s) - computer science , convolutional neural network , artificial intelligence , pattern recognition (psychology) , image (mathematics) , layer (electronics) , deep learning , artificial neural network , contextual image classification , machine learning , scale (ratio) , chemistry , physics , organic chemistry , quantum mechanics
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