6 research outputs found
Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia
Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided
An Overview on Artificial Intelligence Techniques for Diagnosis of Schizophrenia Based on Magnetic Resonance Imaging Modalities: Methods, Challenges, and Future Works
Schizophrenia (SZ) is a mental disorder that typically emerges in late
adolescence or early adulthood. It reduces the life expectancy of patients by
15 years. Abnormal behavior, perception of emotions, social relationships, and
reality perception are among its most significant symptoms. Past studies have
revealed the temporal and anterior lobes of hippocampus regions of brain get
affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and
decreased volume of white and gray matter can be observed due to this disease.
The magnetic resonance imaging (MRI) is the popular neuroimaging technique used
to explore structural/functional brain abnormalities in SZ disorder owing to
its high spatial resolution. Various artificial intelligence (AI) techniques
have been employed with advanced image/signal processing methods to obtain
accurate diagnosis of SZ. This paper presents a comprehensive overview of
studies conducted on automated diagnosis of SZ using MRI modalities. Main
findings, various challenges, and future works in developing the automated SZ
detection are described in this paper