Convolutional Neural Networks in Image Retrieval System
Yogapriya. J1, Dhivya. S2, Suvitha. K3
1J.Yogapriya*, Computer Science and Engineering Department, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India.
2S.Dhivya, Computer Science and Engineering Department, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India.
3K.Suvitha, Computer Science and Engineering Department, Kongunadu College of Engineering and Technology, Trichy, Tamil Nadu, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 123-129 | Volume-9 Issue-5, March 2020. | Retrieval Number: D2001029420 /2020©BEIESP | DOI: 10.35940/ijitee.D2001.039520
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Abstract: Image is an important medium for monitoring the treatment responses of patient’s diseases by the physicians. There could be a tough task to organize and retrieve images in structured manner with respect to incredible increase of images in Hospitals. Text based image retrieval may prone to human error and may have large deviation across different images. Content-Based Medical Image Retrieval(CBMIR) system plays a major role to retrieve the required images from the huge database.Recent advances in Deep Learning (DL) have made greater achievements for solving complex problems in computer vision ,graphics and image processing. The deep architecture of Convolutional Neural Networks (CNN) can combine the low-level features into high-level features which could learn the semantic representation from images. Deep learning can help to extract, select and classify image features, measure the predictive target and gives prediction models to assist physician efficiently. The motivation of this paper is to provide the analysis of medical image retrieval system using CNN algorithm. Keywords : Content Based Medical Image Retrieval (CBMIR), Deep Learning, Convolution Neural Network(CNN), Feature Extraction, Optimization, Classification, Similarity Measurements.
Keywords: Internet of Things, Energy Auditing, Cloud Computing, Sugar Industry, Process Control
Scope of the Article: Machine, Deep Learning with IoT & IoE