Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classifled, stored and retrieved in medical centers. As a result, medical image classiflcation and retrieval has recently gained high interest in the scientiflc community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annotation Competition, the proposed solutions are still far from being su‐ciently accurate for real-life implementations.   summarize the technical details of our experiments for the ImageCLEF 2009 medical image annotation task.  use a direct and two hierarchical classiflcation schemes that employ support vector machines and local binary patterns, which are recently developed low-cost texture descriptors. The direct scheme employs a single SVM to automatically annotate X-ray images. The two proposed hierarchical schemes divide the classiflcation task into sub-problems. The flrst hierarchical scheme exploits ensemble SVMs trained on IRMA sub-codes. The second learns from subgroups of data deflned by frequency of classes. Our experiments show that hierarchical annotation of images by training individual SVMs over each IRMA sub-code dominates its rivals in annotation accuracy with increased process time relative to the direct scheme.
['Devrim Unay', 'Octavian Soldea', 'Süreyya Özögür-Akyüz', 'Müjdat Çetin', 'Aytül Erçil']
I am writing a research paper on a idea Medical Image Retrieval and Automatic Annotation: VPA-SABANCI at ImageCLEF 2009
[{"from": "human", "value": "I am writing a research paper on a idea Medical Image Retrieval and Automatic Annotation: VPA-SABANCI at ImageCLEF 2009"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classifled, stored and retrieved in medical centers. As a result, medical image classiflcation and retrieval has recently gained high interest in the scientiflc community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annotation Competition, the proposed solutions are still far from being su\u2010ciently accurate for real-life implementations.   summarize the technical details of our experiments for the ImageCLEF 2009 medical image annotation task.  use a direct and two hierarchical classiflcation schemes that employ support vector machines and local binary patterns, which are recently developed low-cost texture descriptors. The direct scheme employs a single SVM to automatically annotate X-ray images. The two proposed hierarchical schemes divide the classiflcation task into sub-problems. The flrst hierarchical scheme exploits ensemble SVMs trained on IRMA sub-codes. The second learns from subgroups of data deflned by frequency of classes. Our experiments show that hierarchical annotation of images by training individual SVMs over each IRMA sub-code dominates its rivals in annotation accuracy with increased process time relative to the direct scheme."}]