Image reconstruction using a refined Res-UNet model for medical image retrieval
Measurement of medical data and extraction of medical images with various sensory systems have become a crucial part of diagnosing and treating various diseases. In this contest measurement technology plays a key role in diagnosis. Medical experts usually use previous case studies to identify and deal with the current medical condition. In this context, an expert required to explore the large medical database to search relevant images for the required analysis. Searching such large database and retrieving an image efficiently becomes very tedious task. Therefore, this paper proposed a measurement-based image reconstruction using the refined Res-UNet Framework for Medical Image Retrieval (MIR) for managing such crucial tasks and reduce complexities. The proposed two stage framework consists of an image reconstruction using Res-UNet and index similarity matching of query image with history images. Res-UNet is a vanilla combination of ResNet50 as an encoder that gives latent information of input image, and the decoder from UNet reconstructs the image using latent information. Further, those latent features match similar image features and retrieve indexed images from the medical image database. The efficacy of proposed method was confirmed on benchmark medical image databases such as ILD and VIA/ELCAP-CT for MIR. The proposed framework outperforms the existing methods in the task of MIR.
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