Share Email Print
cover

Proceedings Paper • new

Medical image retrieval using Resnet-18 for clinical diagnosis
Author(s): Swarnambiga Ayyachamy; Varghese Alex; Mahendra Khened; Ganapathy Krishnamurthi
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Advances in medical imaging technologies have led to the generation of large databases with high-resolution image volumes. To retrieve images with pathology similar to the one under examination, we propose a content- based image retrieval framework (CBIR) for medical image retrieval using deep Convolutional Neural Network (CNN). We present retrieval results for medical images using a pre-trained neural network, ResNet-18. A multi- modality dataset that contains twenty-three classes and four modalities including (Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Mammogram (MG), and Positron Emission Tomograph (PET)) are used for demonstrating our method. We obtain an average classification accuracy of 92% and the mean average precision of 0.90 for retrieval. The proposed method can assist in clinical diagnosis and training radiologist.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 1095410 (15 March 2019); doi: 10.1117/12.2515588
Show Author Affiliations
Swarnambiga Ayyachamy, Indian Institute of Technology Madras (India)
Varghese Alex, Indian Institute of Technology Madras (India)
Mahendra Khened, Indian Institute of Technology Madras (India)
Ganapathy Krishnamurthi, Indian Institute of Technology Madras (India)


Published in SPIE Proceedings Vol. 10954:
Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Peter R. Bak, Editor(s)

© SPIE. Terms of Use
Back to Top