In recent years, there has been an adverse rise in neurodegenerative disorders which are commonly found in older people. There has been a lot of research conducted to characterize and diagnose these diseases. Many computational methods, particularly deep learning (DL) models, are being used to diagnose these diseases. In this chapter, the authors conduct an extensive literature survey on neurodegenerative diseases including Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis using deep learning models. Also, they performed a comparative analysis of DL models such as CNN, RNN, ResNet50, DenseNet, etc., which have shown some groundbreaking results for neurodegenerative disease detection and classification using medical image computing including MRI and PET scans. In addition, they have discussed how these DL models are used for feature extraction and to identify disease-relevant biomarkers from high-dimensional data. Furthermore, this study emphasizes the potential of deep learning techniques to revolutionize the diagnosis of neurodegenerative diseases to collaborate in clinical practice.
TopIntroduction
Neurodegenerative diseases are characterized by progressive deterioration and dysfunction of the nervous system. Alzheimer's disease, the most common form of dementia, is characterized by the accumulation of abnormal proteins and the formation of plaques and tangles in the brain, leading to memory loss and cognitive decline. Parkinson's disease is characterized by the loss of dopamine-producing cells in the brain, which causes motor symptoms such as tremors, rigidity, and bradykinesia. Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease, is a progressive neurodegenerative disease that affects the nerve cells responsible for controlling voluntary muscles, leading to muscle weakness, paralysis, and finally respiratory failure.
Traditional Approaches: Treating neurodegenerative diseases, such as Ayurvedic medicine, traditional Chinese medicine, homeopathy, herbal remedies, dietary changes, exercise, and mindfulness, are rooted in ancient practices and natural substances. Although scientific evidence is limited, they may complement conventional treatments, but it is important to consult a physician for guidance and to ensure safety and efficacy in individual cases.
In 2022, there were approximately 55.2 million people living with dementia worldwide, and out of 60% living in low- and middle-income countries. This number is projected to reach 139.5 million by 2050, representing a global health concern. Alzheimer's disease is responsible for 60-70% of dementia cases. Parkinson's: In 2022, there were approximately 8.5 million individuals worldwide living with Parkinson's disease (PD), and this number is projected to increase to 12.9 million by 2040. The exact cause of PD is still unknown but is believed to be the result of a combination of genetic and environmental factors. Amyotrophic Lateral Sclerosis: Amyotrophic lateral sclerosis, has an annual worldwide incidence of about 1.9 per 100,000 people, with a higher prevalence of 4 to 6 per 100,000. It primarily affects individuals over the age of 50 and has a median survival time of 2 to 4 years.
Figure 1.
People living with Alzheimer’s disease
Figure 2.
People living with amyotrophic lateral sclerosis
Figure 3.
People living with Parkinson's disease
TopLiterature Review
Deep learning techniques are known for their ability to provide reliable, consistent, and accurate results. Due to which they are widely applied across multiple domains to solve real-world problems (Hemachandran et al., 2023; Sarker, 2021; Shen et al., 2017; Puttagunta & Ravi, 2021). Researchers have carried out diverse literature that includes datasets, algorithms, and methodology to facilitate future research in the realm of classification and detection of neurodegenerative diseases. Some of the prominent attempts to detect and predict the neurogenerative diseases using deep learning techniques are discussed in Table 1.