Background: 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.
Discussion: 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.
Conclusion: The problem faced by the researchers during image denoising techniques and machine
learning applications for clinical settings have also been discussed.