Translator Disclaimer
Presentation + Paper
15 March 2019 Medical image retrieval using Resnet-18
Author Affiliations +
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Swarnambiga Ayyachamy, Varghese Alex, Mahendra Khened, and Ganapathy Krishnamurthi "Medical image retrieval using Resnet-18", Proc. SPIE 10954, Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications, 1095410 (15 March 2019);

Back to Top