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A review of denoising medical images using machine learning approaches

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Credit: Dr. Prabhpreet Kaur

Machine learning techniques are increasingly demonstrating success in image-based diagnosis, disease detection and disease prognosis. To reduce operator dependency and get better diagnostic accuracy, a computer aided diagnositic (CAD) system is a valuable and beneficial means for breast tumor detection and classification, fetal development and growth, Brain functioning, skin lesions and Lungs diseases [1].

Image denoising using machine learning techniques plays important role in the various application area of medical imaging such as pre-processing (noise removal from Ultrasound (US) images, segmentation (MRI of brain tumors and lung infections using X-rays), Computer aided diagnosis (CAD) for breast cancer, fetus development and many more). Further, denoising of medical images using data mining methods are analyzed.

This paper focuses on the review of various denoising methods along with machine learning approaches to develop a systematic decision for diagnosing and prediction for medical images. The representation of the machine learning i.e. based on various numbers of methods which focuses on prediction, based on known properties learned from the training data has been considered. The observation through literature survey is that the accuracy rate of the existing methods is poor so improvement is required to make them more consistent as Naive Bayes outperforms in accuracy as compare to kNN and SVM. The most important point is that the benchmark database of ultrasound scanned images should be accessible to the public to compare and dynamically evaluate different algorithms based on CAD systems.

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For more information, please visit: http://www.eurekaselect.com/152027/article

Reference:

Kaur P. (2017). A Review of Denoising Medical Images using Machine Learning Approaches, Current Medical Imaging Reviews., DOI: 10.2174/1573405613666170428154156

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Related Journal Article

http://dx.doi.org/10.2174/1573405613666170428154156

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