Medical Image Quality Assessment Using CSO Based Deep Neural Network
Abstract
This manuscript proposed a hybrid method of Deep Neural Network (DNN) and Cuckoo Search Optimization (CSO) with No-Reference Image Quality Assessment (NR-IQA) for achieving high accuracy, low computational complexity, flexibility and etc. of a medical image. NR-IQA is proposed due to till now there is no perfect reference image for finding the quality of real time medical imaging. It is an effective method for assessing the real-world medical images. The proposed method takes the distorted image as an input and estimate the quality of the image without the assistance of reference image. The techniques CSO and DNN with NR-IQA produces the quality of the image with high quality score and low Mean Square Error (MSE). Also, the proposed method is used to improve the quality score thereby improving the quality of the image. So that the resultant image has good visual properties which is useful for the analysis of further medical proceedings. The simulation result shows that the proposed system improves the quality score by 8% when compared to the other existing systems. The SROCC value can be increased as 6%, 14%, 6 and 2% for the different existing methods such as NR-BIQA, SBVQP-ML, PTQL/PTVC and NR-SIQA (3D) respectively.
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Index Terms
- Medical Image Quality Assessment Using CSO Based Deep Neural Network
Index terms have been assigned to the content through auto-classification.
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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.
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Plenum Press
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Published: 01 November 2018
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