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25 March 2016 A handheld computer-aided diagnosis system and simulated analysis
Mingjian Su, Xuejun Zhang, Brent Liu, Kening Su, Ryan Louie
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Proceedings Volume 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations; 97890P (2016) https://doi.org/10.1117/12.2216421
Event: SPIE Medical Imaging, 2016, San Diego, California, United States
Abstract
This paper describes a Computer Aided Diagnosis (CAD) system based on cellphone and distributed cluster. One of the bottlenecks in building a CAD system for clinical practice is the storage and process of mass pathology samples freely among different devices, and normal pattern matching algorithm on large scale image set is very time consuming. Distributed computation on cluster has demonstrated the ability to relieve this bottleneck. We develop a system enabling the user to compare the mass image to a dataset with feature table by sending datasets to Generic Data Handler Module in Hadoop, where the pattern recognition is undertaken for the detection of skin diseases. A single and combination retrieval algorithm to data pipeline base on Map Reduce framework is used in our system in order to make optimal choice between recognition accuracy and system cost. The profile of lesion area is drawn by doctors manually on the screen, and then uploads this pattern to the server. In our evaluation experiment, an accuracy of 75% diagnosis hit rate is obtained by testing 100 patients with skin illness. Our system has the potential help in building a novel medical image dataset by collecting large amounts of gold standard during medical diagnosis. Once the project is online, the participants are free to join and eventually an abundant sample dataset will soon be gathered enough for learning. These results demonstrate our technology is very promising and expected to be used in clinical practice.
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Mingjian Su, Xuejun Zhang, Brent Liu, Kening Su, and Ryan Louie "A handheld computer-aided diagnosis system and simulated analysis", Proc. SPIE 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations, 97890P (25 March 2016); https://doi.org/10.1117/12.2216421
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KEYWORDS
Skin
Computer aided diagnosis and therapy
Computing systems
Distributed computing
Feature extraction
Image processing
Image retrieval
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