A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion

Lilia Lazli,Mounir Boukadoum,Otmane Ait Mohamed

doi:10.3390/app10051894

Abstract

Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.

Highlights

In the fields of research in medical imaging and diagnostic radiology, computer-aided diagnosis (CAD) has had major interest and development during the last two decades [1–9]
The rest of the article is organized as follows: In Section 2, first, we describe the foundations of a CAD system, we analyze the research work already carried out using the magnetic resonance imaging (MRI) mono modality
We provide an overview of the applicability and progress of information fusion techniques in medical imaging, highlighting the disadvantages and advantages of the methods suggested by researchers in the context of multimodal fusion

Summary

Introduction

In the fields of research in medical imaging and diagnostic radiology, computer-aided diagnosis (CAD) has had major interest and development during the last two decades [1–9]. Sci. 2020, 10, 1894 technology is to support radiologists using computer systems in their interpretation of brain images and in the diagnosis of brain diseases. The CAD system provides a second opinion, it makes it possible to analyze medical images thanks to its techniques of pattern recognition and machine learning. This alleviates the fatigue of the radiologist and the burden of the workload, due to the overloaded data. As a result, this technology has the ability to improve diagnostic consistency and accuracy in order to decrease the rate of false negatives, including estimating the extent of the disease

Objectives

Findings

Discussion

Conclusion