Machine Learning Techniques for Medical Image Processing

In last decade, machine learning (ML) techniques increased the capability to automatically learn the experience without being explicitly programmed. Different machine learning methods are used for a number of tasks such as image processing, predictive analytics, data mining, and are used for classification, regression, clustering, and dimensionality reduction. ML is widely utilized in medical imaging research field. This paper introduces a survey on ML used in medical image processing and it focuses on two main types (supervised and unsupervised learning) to importance them in medical image processing with explains the foremost important algorithms of machine learning, discussing the most important advantages and drawbacks of applying Machine Learning techniques in medical imaging. In addition, some common algorithms were applied like k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Logistic Regression and Random Forest on medical dataset in order to check the efficiency of algorithms.