Correlating the Machine Learning Models for Automatic Error Detection and Correction in Medical Images
| Autor: | NK Roopa, G S Mamatha |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Process (engineering) business.industry 02 engineering and technology Optical character recognition Machine learning computer.software_genre Image (mathematics) 03 medical and health sciences 0302 clinical medicine Optical imaging 0202 electrical engineering electronic engineering information engineering Medical imaging Learning methods 020201 artificial intelligence & image processing 030212 general & internal medicine Artificial intelligence Error detection and correction business computer Character recognition |
| Zdroj: | 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). |
| DOI: | 10.1109/icimia48430.2020.9074862 |
| Popis: | Medical image has always been considered as a significant research area when it comes to advanced analysis in the critical disease diagnosis process. There has been massive archival of research works based on the medical image concerning the classification and analysis. In this regard, this paper has identified two core techniques that have been frequently used over images viz. i) optical character recognition and ii) machine learning. Both the system has its own benefits and limitation as witnessed in many research works. The contribution of this paper is to find out an essential correlation between these two frequently used models over images so that a novel form of solution can be developed in future applications based on medical images. This paper briefs all the essential information available on the existing approaches of both the system focusing mainly on both the error correction methods and learning methods. It is anticipated that the finding of this paper will assist in deploying a better decision to choose machine learning and it's applicability shortly. |
| Databáze: | OpenAIRE |
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