Most Accessed Articles | A Review of Denoising Medical Images Using Machine Learning Approaches

Author(s): Prabhpreet Kaur*, Gurvinder Singh, Parminder Kaur.

Journal Name: Current Medical Imaging

 

This paper attempts to identify suitable Machine Learning (ML) approach for image denoising of radiology based medical application. The Identification of ML approach is based on (i) Review of ML approach for denoising (ii) Review of suitable Medical Denoising approach.

The review focuses on six application of radiology: Medical Ultrasound (US) for fetus development, US Computer Aided Diagnosis (CAD) and detection for breast, skin lesions, brain tumor MRI diagnosis, X-Ray for chest analysis, Breast cancer using MRI imaging. This survey identifies the ML approach with better accuracy for medical diagnosis by radiologists. The image denoising approaches further includes basic filtering techniques, wavelet medical denoising, curvelet and optimization techniques. In most of the applications, the machine learning performance is better than the conventional image denoising techniques. For fast and computational results the radiologists are using the machine learning methods on MRI, US, X-Ray and Skin lesion images. The characteristics and contributions of different ML approaches are considered in this paper. Read out full article here: http://www.eurekaselect.com/152027

 

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Author: Bentham Science Publishers

A major STM journal publisher of more than 100 online and print journals and related print/online book series, Bentham Science answers the information needs of scientists in the fields of pharmaceutical, biomedical, medical, engineering, technology, computer and social sciences.

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