Machine Learning Approaches for Efficient Analysis of Neuroimaging Techniques

Abstract

Machine Learning has a significant role in each person’s daily life and plays a vital role in making life easier by contributing to various models where the machines learn and do the tasks better. Much research and development around machine learning algorithms and their applications are happening for classifying and clustering multiple types of data in several domains. Health care research also impacts machine learning in analysing different data for patients. Different types of image and Neuroimaging data analysis are the areas where a significant amount of research is happening with healthcare and machine learning. Neuroimaging data obtained from the imaging techniques like MRI, CT, fMRI, PET, and other techniques help doctors identify various disorders. Commonly studied diseases with the help of neuroimaging data include the disorders like Alzheimer’s, MCI, Parkinson’s Disease, and Autism. Machine learning algorithms are developed for the straightforward interpretation of neuroimaging data and identifying neurological disorders. Interpreting neuroimaging takes a lot of assumptions and risks by doctors; commonly used and developed Machine Learning models are CNN, SVM, ANN, and Deep CNN. The use of proper machine learning models can help doctors to validate their assumptions in critical conditions. The paper focuses on a survey of various approaches by researchers to bring out neuroimaging analysis models and identify effective models. The research also covers the multiple diseases and the best models available for detecting the disorders. This research aims to identify the challenges various researchers face while creating the models and the limitations of their models, and how machine learning algorithms could effectively analyse neuroimages.