Computer-Aided Diagnosis in Neuroimaging

This chapter is intended to provide an overview to the most used methods for computer-aided diagnosis in neuroimaging and its application to neurodegenerative diseases. The fundamental preprocessing steps, and how they are applied to different image modalities, will be thoroughly presented. We introduce a number of widely used neuroimaging analysis algorithms, together with a wide overview on the recent advances in brain imaging processing. Finally, we provide a general conclusion on the state of the art in brain imaging processing and possible future developments

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applica-tions in medication is a venue internationally, for medical trade and creating an ener-getic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxono-my of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain

Improvement of alzheimer disease diagnosis accuracy using ensemble methods

Nowadays, there is a significant increase in the medical data that we should take advantage of that. The application of the machine learning via the data mining processes, such as data classification depends on using a single classification algorithm or those complained as ensemble models. The objective of this work is to improve the classification accuracy of previous results for Alzheimer disease diagnosing. The Decision Tree algorithm with three types of ensemble methods combined, which are Boosting, Bagging and Stacking. The clinical dataset from the Open Access Series of Imaging Studies (OASIS) was used in the experiments. The experimental results of the proposed approach were better than the previous work results. Where the Random Forest (Bagging) achieved the highest accuracy among all algorithms with 90.69%, while the lowest one was Stacking with 79.07%. All these results generated in this paper are higher in accuracy than that done before

Abordagem CNN 2D estendida para o diagnóstico da doença de Alzheimer através de imagens de ressonância magnética estrutural

Orientadores: Leticia Rittner, Roberto de Alencar LotufoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A doença de Alzheimer (AD - Alzheimer's disease) é um tipo de demência que afeta milhões de pessoas em todo o mundo. Até o momento, não há cura para a doença e seu diagnóstico precoce tem sido uma tarefa desafiadora. As técnicas atuais para o seu diagnóstico têm explorado as informações estruturais da Imagem por Ressonância Magnética (MRI - Magnetic Resonance Imaging) em imagens ponderadas em T1. Entre essas técnicas, a rede neural convolucional (CNN - Convolutional Neural Network) é a mais promissora e tem sido usada com sucesso em imagens médicas para uma variedade de aplicações devido à sua capacidade de extração de características. Antes do grande sucesso do aprendizado profundo e das CNNs, os trabalhos que objetivavam classificar os diferentes estágios de AD exploraram abordagens clássicas de aprendizado de máquina e uma meticulosa extração de características, principalmente para classificar testes binários. Recentemente, alguns autores combinaram técnicas de aprendizagem profunda e pequenos subconjuntos do conjunto de dados públicos da Iniciativa de Neuroimagem da Doença de Alzheimer (ADNI - Alzheimer's Disease Neuroimaging Initiative) para prever um estágio inicial da doença explorando abordagens 3D CNN geralmente combinadas com arquiteturas de auto-codificador convolucional 3D. Outros também exploraram uma abordagem de CNN 3D combinando-a ou não com uma etapa de pré-processamento para a extração de características. No entanto, a maioria desses trabalhos focam apenas na classificação binária, sem resultados para AD, comprometimento cognitivo leve (MCI - Mild Cognitive Impairment) e classificação de sujeitos normais (NC - Normal Control). Nosso principal objetivo foi explorar abordagens de CNN 2D para a tarefa de classificação das 3 classes usando imagens de MRI ponderadas em T1. Como objetivo secundário, preenchemos algumas lacunas encontradas na literatura ao investigar o uso de arquiteturas CNN 2D para o nosso problema, uma vez que a maioria dos trabalhos explorou o aprendizado de máquina clássico ou abordagens CNN 3D. Nossa abordagem CNN 2D estendida explora as informações volumétricas dos dados de ressonância magnética, mantendo baixo custo computacional associado a uma abordagem 2D, quando comparados às abordagens 3D. Além disso, nosso resultado supera as outras estratégias para a classificação das 3 classes e comparando o desempenho de nosso modelo com os métodos tradicionais de aprendizado de máquina e 3D CNN. Também investigamos o papel de diferentes técnicas amplamente utilizadas em aplicações CNN, por exemplo, pré-processamento de dados, aumento de dados, transferência de aprendizado e adaptação de domínio para um conjunto de dados brasileiroAbstract: Alzheimer's disease (AD) is a type of dementia that affects millions of people around the world. To date, there is no cure for Alzheimer's and its early-diagnosis has been a challenging task. The current techniques for Alzheimer's disease diagnosis have explored the structural information of Magnetic Resonance Imaging (MRI) in T1-weighted images. Among these techniques, deep convolutional neural network (CNN) is the most promising one and has been successfully used in medical images for a variety of applications due to its ability to perform features extraction. Before the great success of deep learning and CNNs, the works that aimed to classify the different stages of AD explored classic machine learning approaches and a meticulous feature engineering extraction, mostly to classify binary tasks. Recently, some authors have combined deep learning techniques and small subsets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) public dataset, to predict an early-stage of AD exploring 3D CNN approaches usually combined with 3D convolutional autoencoder architectures. Others have also investigated a 3D CNN approach combining it or not with a pre-processing step for the extraction of features. However, the majority of these papers focus on binary classification only, with no results for Alzheimer's disease, Mild Cognitive Impairment (MCI), and Normal Control (NC) classification. Our primary goal was to explore 2D CNN approaches to tackle the 3-class classification using T1-weighted MRI. As a secondary goal, we filled some gaps we found in the literature by investigating the use of 2D CNN architectures to our problem, since most of the works either explored traditional machine learning or 3D CNN approaches. Our extended-2D CNN explores the MRI volumetric data information while maintaining the low computational costs associated with a 2D approach when compared to 3D-CNNs. Besides, our result overcomes the other strategies for the 3-class classification while analyzing the performance of our model with traditional machine-learning and 3D-CNN methods. We also investigated the role of different widely used techniques in CNN applications, for instance, data pre-processing, data augmentation, transfer-learning, and domain-adaptation to a Brazilian datasetMestradoEngenharia de ComputaçãoMestra em Engenharia Elétrica168468/2017-4  CNP

3D Convolution Neural Networks for Medical Imaging; Classification and Segmentation : A Doctor’s Third Eye

Master's thesis in Information- and communication technology (IKT591)In this thesis, we studied and developed 3D classification and segmentation models for medical imaging. The classification is done for Alzheimer’s Disease and segmentation is for brain tumor sub-regions. For the medical imaging classification task we worked towards developing a novel deep architecture which can accomplish the complex task of classifying Alzheimer’s Disease volumetrically from the MRI scans without the need of any transfer learning. The experiments were performed for both binary classification of Alzheimer’s Disease (AD) from Normal Cognitive (NC), as well as multi class classification between the three stages of Alzheimer’s called NC, AD and Mild cognitive impairment (MCI). We tested our model on the ADNI dataset and achieved mean accuracy of 94.17% and 89.14% for binary classification and multiclass classification respectively. In the second part of this thesis which is segmentation of tumors sub-regions in brain MRI images we studied some popular architecture for segmentation of medical imaging and inspired from them, proposed our architecture of end-to-end trainable fully convolutional neural net-work which uses attention block to learn the localization of different features of the multiple sub-regions of tumor. Also experiments were done to see the effect of weighted cross-entropy loss function and dice loss function on the performance of the model and the quality of the output segmented labels. The results of evaluation of our model are received through BraTS’19 dataset challenge. The model is able to achieve a dice score of 0.80 for the segmentation of whole tumor, and a dice scores of 0.639 and 0.536 for other two sub-regions within the tumor on validation data. In this thesis we successfully applied computer vision techniques for medical imaging analysis. We show the huge potential and numerous benefits of deep learning to combat and detect diseases opens up more avenues for research and application for automating medical imaging analysis

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis : Principles and Recent Advances

This work was supported in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF 2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program under Grant 20212023.Peer reviewedPublisher PD

Computational Intelligent Models for Alzheimer's Prediction Using Audio Transcript Data

Alzheimer's dementia (AD) is characterized by memory loss, which is one of the earliest symptoms to develop. In this study, we investigated audio transcript data of patients with Alzheimer's dementia. The study involved the use of three intelligent computational approaches: conventional machine learning (Support Vector Machine, Random Forest, Decision Tree), sequential deep learning (LSTM, bidirectional LSTM, CNN-LSTM), and transfer learning (BERT, XLNet) models for automatic detection of linguistic indicators for early diagnosis of Alzheimer's dementia. These models were trained on the DementiaBank clinical transcript dataset. The grid search tuning approach is used for tuning the values of the hyperparameters. Text vectorization is done using the Term Frequency-Inverse Document Frequency (TF-IDF) information retrieval approach. TF-IDF is based on the Bag of Words (BoW) paradigm, which deals with the less and more relevant words in a transcript. Results were evaluated and compared using several performance metrics. The state-of-the-art techniques implemented on DementiaBank dataset in our methodology achieved better performance in terms of accuracy. Transfer learning models showed better classification results in comparison to sequential deep learning models. However, sequential deep learning models outperformed traditional machine learning models. Overall, in terms of accuracy, BERT and XLNet were the most accurate, with accuracy of 93 % and 92 %, respectively

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject