Computer Vision with Deep Learning Techniques for Neurodegenerative Diseases Analysis Using Neuroimaging: A Survey

Vij, Richa; Arora, Sakshi

doi:10.1007/978-981-16-2597-8_15

Cited by 14 publications

(3 citation statements)

References 26 publications

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“…The diagnosis of brain disorders including PD, AD, and schizophrenia (SZ) is often greatly aided by multimodality neuroimaging data [ 184 ]. Various clinical investigations have described the reliable detection of PD using a combination of neuroimaging modalities, such as EEG-fMRI [ 185 , 186 , 187 , 188 ], MRI-PET [ 189 , 190 , 191 , 192 ], fMRI-MEG [ 193 , 194 ], and fMRI-sMRI [ 195 , 196 , 197 ]. Diagnosis of PD using multimodality neuroimaging data is complicated and time-consuming for doctors, despite all the advantages.…”

Section: Discussion: Challenges and Recommendationsmentioning

confidence: 99%

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

et al. 2022

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According to the World Health Organization (WHO), Parkinson’s disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer’s disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis ofthe use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson’s research.

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Section: Discussion: Challenges and Recommendationsmentioning

confidence: 99%

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

et al. 2022

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“…Artificial intelligence approaches, with machine learning and deep learning, and computer vision techniques, are used for the prognosis of neurodegenerative diseases and analysis. The data for this analysis is usually collected from neuroimaging techniques [9]. The commonly used imaging techniques for the different disease prognoses include Magnetic Resonance Imaging, Single Photon Emission Computed Tomography, Computed Tomography, and Positron Emission Tomography.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Approaches for Efficient Analysis of Neuroimaging Techniques

Joseph

Chandra

2022

SHS Web Conf.

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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.

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“…Artificial intelligence and, especially, Convolutional Neural Networks (CNN) [8] have proved to be state-of-the-art methods for medical imagining analysis [15,[18][19][20]. In recent years, the value of deep learning empowered computerassisted diagnosis has been established in dermatological imaging-based decisionmaking models [5].…”

Section: Introductionmentioning

confidence: 99%

Contrastive and Attention-Based Multiple Instance Learning for the Prediction of Sentinel Lymph Node Status from Histopathologies of Primary Melanoma Tumours

Hernández-Pérez

Escudero

Puig

et al. 2022

Lecture Notes in Computer Science

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Sentinel lymph node status is a crucial prognosis factor for melanomas; nonetheless, the invasive surgery required to obtain it always puts the patient at risk. In this study, we develop a Deep Learning-based approach to predict lymph node metastasis from Whole Slide Images of primary tumours. Albeit very informative, these images come with complexities that hamper their use in machine learning applications, namely their large size and limited datasets. We propose a pre-training strategy based on self-supervised contrastive learning to extract better image feature representations and an attention-based Multiple Instance Learning approach to enhance the model's performance. With this work, we quantitatively demonstrate that combining both methods improves various classification metrics and qualitatively show that contrastive learning encourages the network to output higher attention scores to tumour tissue and lower scores to image artifacts.

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Computer Vision with Deep Learning Techniques for Neurodegenerative Diseases Analysis Using Neuroimaging: A Survey

Cited by 14 publications

References 26 publications

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

Machine Learning Approaches for Efficient Analysis of Neuroimaging Techniques

Contrastive and Attention-Based Multiple Instance Learning for the Prediction of Sentinel Lymph Node Status from Histopathologies of Primary Melanoma Tumours

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