A Comparative Study of Different ML Techniques for Medical Image Analysis

Abstract

In medicine field machine learning (ML) techniques for medical images play a vital role in analysing the monitoring modalities like magnetic resonance imaging (MRI), electroencephalography (EEG), Electrocardio graph (ECG) etc. to detect a disease. The techniques are classified as supervised ML technique and unsupervised ML technique. The various methods like support vector machine, density estimation, hierarchical clustering and few more are detailed. Among all these, the efficient technique is putforth which is feasible for medical field. A generalized model is proposed for the workflow of ML algorithms. The different procedures like feature extraction, feature selection, classification, detection stages are explained. Hence, a feasible method of ML can be chosen to implement in various fields depending on the characteristics of the data.