An Enhanced Framework for Content Based Medical Image Retrieval using Deep Neural Network
Haripriya P1, Porkodi R2
1P Haripriya, Department of Computer Science, Bharathiar University, Coimbatore (Tamil Nadu), India.
2R. Porkodi, Associate Professor, Department of Computer Science, Bharathiar University, Coimbatore (Tamil Nadu), India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 01 September 2019 | Manuscript Published on 14 September 2019 | PP: 239-244 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10540785S319/19©BEIESP | DOI: 10.35940/ijeat.E1054.0785S319
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Abstract: As the technology growth fuelled by low cost tech in the areas of compute, storage the need for faster retrieval and processing of data is becoming paramount for organizations. The medical domain predominantly for medical image processing with large size is critical for making life critical decisions. Healthcare community relies upon technologies for faster and accurate retrieval of images. Traditional, existing problem of efficient and similar medical image retrieval from huge image repository are reduced by Content Based Image Retrieval (CBIR) . The major challenging is an semantic gap in CBIR system among low and high level image features. This paper proposed, enhanced framework for content based medical image retrieval using DNN to overcome the semantic gap problem. It is outlines the steps which can be leveraged to search the historic medical image repository with the help of image features to retrieve closely relevant historic image for faster decision making from huge volume of database. The proposed system is assessed by inquisitive amount of images and the performance efficiency is calculated by precision and recall evaluation metrics. Experimental results obtained the retrieval accuracy is 79% based on precision and recall and this approach is preformed very effectively for image retrieval performance.
Keywords: CBIR, DICOM, DNN, Semantic Gap.
Scope of the Article: Image Processing and Pattern Recognition