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With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the "semantic gap" that exists between the low level visual information captured by imaging devices and high level semantic information perceived by the human. Using deep convolution neuraldoi:10.2298/csis171210020q fatcat:t7hons6mjfevncwqaomsx3pqxq