A genetic optimized neural network for image retrieval in telemedicine

Telemedicine integrates information and communication technologies in providing clinical services to health professionals in different places. Medical images are required to be transmitted for diagnosis and opinion as part of the telemedicine process. Thus, telemedicine challenges include limited bandwidth and large amount of diagnostic data. Content-based image retrieval is used in retrieving relevant images from the database, and image compression addresses the problem of limited bandwidth. This paper proposes a novel method to enable telemedicine using soft computing approaches. In the present study, images are compressed to minimize bandwidth utilization, and compressed images similar to the query medical image are retrieved using a novel feature extraction and a genetic optimized classifier. The effectiveness of compressed image retrieval on magnetic resonance scan images of stroke patients is presented in this study.

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