Image Classification using Supervised Convolutional Neural Network
Saripalli Sri Sravya1, Kalva Sri Rama Krishna2, Pallikonda Sarah Suhasini3
1Saripalli Sri Sravya, Student, Department of Engineering and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India
2Kalva Sri Rama Krishna, Professor, Department of Engineering and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India
3Pallikonda Sarah Suhasini, Associate Professor, Department of Engineering and Communication Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, India.
Manuscript received on 11 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 4505-4507 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3486078219/19©BEIESP | DOI: 10.35940/ijrte.B3486.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Index Terms: Deep Learning, Supervised Convolution Neural Network (SCNN), Image Classification, Supervised Learning.
Scope of the Article: Classification