{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/medical-image-retrieval-using-deep","title":"Medical Image Retrieval using Deep Convolutional Neural Network","arxiv_id":"1703.08472","date":"2017-03-24","proceeding":null,"authors":["Adnan Qayyum","Syed Muhammad Anwar","Muhammad Awais","Muhammad Majid"],"abstract":"With a widespread use of digital imaging data in hospitals, the size of\nmedical image repositories is increasing rapidly. This causes difficulty in\nmanaging and querying these large databases leading to the need of content\nbased medical image retrieval (CBMIR) systems. A major challenge in CBMIR\nsystems is the semantic gap that exists between the low level visual\ninformation captured by imaging devices and high level semantic information\nperceived by human. The efficacy of such systems is more crucial in terms of\nfeature representations that can characterize the high-level information\ncompletely. In this paper, we propose a framework of deep learning for CBMIR\nsystem by using deep Convolutional Neural Network (CNN) that is trained for\nclassification of medical images. An intermodal dataset that contains twenty\nfour classes and five modalities is used to train the network. The learned\nfeatures and the classification results are used to retrieve medical images.\nFor retrieval, best results are achieved when class based predictions are used.\nAn average classification accuracy of 99.77% and a mean average precision of\n0.69 is achieved for retrieval task. The proposed method is best suited to\nretrieve multimodal medical images for different body organs.","url_abs":"http://arxiv.org/abs/1703.08472v1","url_pdf":"http://arxiv.org/pdf/1703.08472v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"medical-image-retrieval-using-deep","repo_url":"https://github.com/himanshunaidu/cnn_adnan_fruit","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"medical-image-retrieval","task_name":"Medical Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1703.08472","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}