A reproducible 3D convolutional neural network with dual attention module (3D-DAM) for Alzheimer's disease classification

Alzheimer's disease is one of the most common types of neurodegenerative disease, characterized by the accumulation of amyloid-beta plaque and tau tangles. Recently, deep learning approaches have shown promise in Alzheimer's disease diagnosis. In this study, we propose a reproducible model that utilizes a 3D convolutional neural network with a dual attention module for Alzheimer's disease classification. We trained the model in the ADNI database and verified the generalizability of our method in two independent datasets (AIBL and OASIS1). Our method achieved state-of-the-art classification performance, with an accuracy of 91.94% for MCI progression classification and 96.30% for Alzheimer's disease classification on the ADNI dataset. Furthermore, the model demonstrated good generalizability, achieving an accuracy of 86.37% on the AIBL dataset and 83.42% on the OASIS1 dataset. These results indicate that our proposed approach has competitive performance and generalizability when compared to recent studies in the field

Robust Brain Tissue Segmentation in AD Using Comparative Linear Transformation and Deep Learning

As a progressive neurological disease, Alzheimer's disease (AD), if no preventative measures are   taken, can result in dementia and a severe decline in brain function, making it difficult to perform basic tasks. Over 1 in 9 people suffer from dementia caused by Alzheimer's disease and require uncompensated care. The hippocampus is extracted from MRI scans of the brain via image segmentation have been useful for diagnosing Alzheimer's disease (AD).The segmentation of the CSF region in brain MRI is critical for analyzing the stages of AD. The extraction of Hippocampus from an MRI of the brain is greatly influenced by the contrast of the images. Using comparative linear transformation in the horizontal and vertical dimensions as well as statistical edge-based features, this article proposes a robust method for segmentation technique for the extraction of Hippocampus from brain MRI. These transformations aid in balancing the brain image's thin and dense fluid extractions. Through use of the ADNI dataset, the proposed approach had a 99% success rate in segmentation

Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

Development of Gaussian Learning Algorithms for Early Detection of Alzheimer\u27s Disease

Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer\u27s Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot be reversed or stopped. This research aims to develop Gaussian learning algorithms, natural language processing (NLP) techniques, and mathematical models to effectively delineate the MCI participants from the cognitively normal (CN) group, and identify the most significant brain regions and patterns of changes associated with the progression of AD. The focus will be placed on the earliest manifestations of the disease (early MCI or EMCI) to plan for effective curative/therapeutic interventions and protocols. Multiple modalities of biomarkers have been found to be significantly sensitive in assessing the progression of AD. In this work, several novel multimodal classification frameworks based on proposed Gaussian Learning algorithms are created and applied to neuroimaging data. Classification based on the combination of structural magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers is seen as the most reliable approach for high-accuracy classification. Additionally, changes in linguistic complexity may provide complementary information for the diagnosis and prognosis of AD. For this research endeavor, an NLP-oriented neuropsychological assessment is developed to automatically analyze the distinguishing characteristics of text data in MCI group versus those in CN group. Early findings suggest significant linguistic differences between CN and MCI subjects in terms of word usage, vocabulary, recall, fragmented sentences. In summary, the results obtained indicate a high potential of the neuroimaging-based classification and NLP-oriented assessment to be utilized as a practically computer aided diagnosis system for classification and prediction of AD and its prodromal stages. Future work will ultimately focus on early signs of AD that could help in the planning of curative and therapeutic intervention to slow the progression of the disease

DEEP-AD: The deep learning model for diagnostic classification and prognostic prediction of alzheimer's disease

In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical judgment regarding the early detection of AD by using DL. To that aim, the system design research methodology is suggested in this dissertation for achieving three goals. The first goal is to investigate the DL models that have performed well at identifying patterns associated with AD, as well as the accuracy so far attained, limitations, and gaps. A systematic review of the literature (SLR) revealed a shortage of empirical studies on the early identification of AD through DL. In this regard, thirteen empirical studies were identified and examined. We concluded that three-dimensional (3D) DL models have been generated far less often and that their performance is also inadequate to qualify them for clinical trials. The second goal is to provide the neuroradiologist with the computer-interpretable information they need to analyze neuroimaging biomarkers. Given this context, the next step in this dissertation is to find the optimum DL model to analyze neuroimaging biomarkers. It has been achieved in two steps. In the first step, eight state-of-the-art DL models have been implemented by training from scratch using end-to-end learning (E2EL) for two binary classification tasks (AD vs. CN and AD vs. stable MCI) and compared by utilizing MRI scans from the publicly accessible datasets of neuroimaging biomarkers. Comparative analysis is carried out by utilizing efficiency-effects graphs, comprehensive indicators, and ranking mechanisms. For the training of the AD vs. sMCI task, the EfficientNet-B0 model gets the highest value for the comprehensive indicator and has the fewest parameters. DenseNet264 performed better than the others in terms of evaluation matrices, but since it has the most parameters, it costs more to train. For the AD vs. CN task by DenseNet264, we achieved 100% accuracy for training and 99.56% accuracy for testing. However, the classification accuracy was still only 82.5% for the AD vs. sMCI task. In the second step, fusion of transfer learning (TL) with E2EL is applied to train the EfficientNet-B0 for the AD vs. sMCI task, which achieved 95.29% accuracy for training and 93.10% accuracy for testing. Additionally, we have also implemented EfficientNet-B0 for the multiclass AD vs. CN vs. sMCI classification task with E2EL to be used in ensemble of models and achieved 85.66% training accuracy and 87.38% testing accuracy. To evaluate the model’s robustness, neuroradiologists must validate the implemented model. As a result, the third goal of this dissertation is to create a tool that neuroradiologists may use at their convenience. To achieve this objective, this dissertation proposes a web-based application (DEEP-AD) that has been created by making an ensemble of Efficient-Net B0 and DenseNet 264 (based on the contribution of goal 2). The accuracy of a DEEP-AD prototype has undergone repeated evaluation and improvement. First, we validated 41 subjects of Spanish MRI datasets (acquired from HT Medica, Madrid, Spain), achieving an accuracy of 82.90%, which was later verified by neuroradiologists. The results of these evaluation studies showed the accomplishment of such goals and relevant directions for future research in applied DL for the early detection of AD in clinical settings.En términos de contexto, el objetivo de esta tesis es ayudar a los neurorradiólogos en su juicio clínico sobre la detección precoz de la AD mediante el uso de DL. Para ello, en esta tesis se propone la metodología de investigación de diseño de sistemas para lograr tres objetivos. El segundo objetivo es proporcionar al neurorradiólogo la información interpretable por ordenador que necesita para analizar los biomarcadores de neuroimagen. Dado este contexto, el siguiente paso en esta tesis es encontrar el modelo DL óptimo para analizar biomarcadores de neuroimagen. Esto se ha logrado en dos pasos. En el primer paso, se han implementado ocho modelos DL de última generación mediante entrenamiento desde cero utilizando aprendizaje de extremo a extremo (E2EL) para dos tareas de clasificación binarias (AD vs. CN y AD vs. MCI estable) y se han comparado utilizando escaneos MRI de los conjuntos de datos de biomarcadores de neuroimagen de acceso público. El análisis comparativo se lleva a cabo utilizando gráficos de efecto-eficacia, indicadores exhaustivos y mecanismos de clasificación. Para el entrenamiento de la tarea AD vs. sMCI, el modelo EfficientNet-B0 obtiene el valor más alto para el indicador exhaustivo y tiene el menor número de parámetros. DenseNet264 obtuvo mejores resultados que los demás en términos de matrices de evaluación, pero al ser el que tiene más parámetros, su entrenamiento es más costoso. Para la tarea AD vs. CN de DenseNet264, conseguimos una accuracy del 100% en el entrenamiento y del 99,56% en las pruebas. Sin embargo, la accuracy de la clasificación fue sólo del 82,5% para la tarea AD vs. sMCI. En el segundo paso, se aplica la fusión del aprendizaje por transferencia (TL) con E2EL para entrenar la EfficientNet-B0 para la tarea AD vs. sMCI, que alcanzó una accuracy del 95,29% en el entrenamiento y del 93,10% en las pruebas. Además, también hemos implementado EfficientNet-B0 para la tarea de clasificación multiclase AD vs. CN vs. sMCI con E2EL para su uso en conjuntos de modelos y hemos obtenido una accuracy de entrenamiento del 85,66% y una precisión de prueba del 87,38%. Para evaluar la solidez del modelo, los neurorradiólogos deben validar el modelo implementado. Como resultado, el tercer objetivo de esta disertación es crear una herramienta que los neurorradiólogos puedan utilizar a su conveniencia. Para lograr este objetivo, esta disertación propone una aplicación basada en web (DEEP-AD) que ha sido creada haciendo un ensemble de Efficient-Net B0 y DenseNet 264 (basado en la contribución del objetivo 2). La accuracy del prototipo DEEP-AD ha sido sometida a repetidas evaluaciones y mejoras. En primer lugar, validamos 41 sujetos de conjuntos de datos de MRI españoles (adquiridos de HT Medica, Madrid, España), logrando una accuracy del 82,90%, que posteriormente fue verificada por neurorradiólogos. Los resultados de estos estudios de evaluación mostraron el cumplimiento de dichos objetivos y las direcciones relevantes para futuras investigaciones en DL, aplicada en la detección precoz de la AD en entornos clínicos.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione

Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective

Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinician

Detecting Alzheimer\u27s Disease using Artificial Neural Networks

This project aims to use artificial neural networks (ANN) in order to detect Alzheimer’s disease. More specifically, convolutional neural networks (CNN) will be utilized as this is the most common ANN and has been used in many different image processing applications. The purpose of using artificial neural networks as a detect method is so that an intelligent way for image and signal analysis can be used. A software that implements CNN will be developed so that users in medical settings can utilize this software to detect Alzheimer’s in patients. The input for this software will be the patient’s MRI scans. In addition, this is a project that is relevant with the current trends of an increase in development surrounding artificial intelligence. As technology has become more advanced, there has been an increase in medical developments as well. From the simulation, the hyperbolic tangent activation function provided the best results. The performance resulting from the two classifications when using the hyperbolic tangent function, on average was validation best accuracy of 81.10%, validation stopped tuning accuracy of 81.10%, training best accuracy of 100.00%, testing best accuracy of 68.94%, F-1 score of 70.06%, precision of 71.00%, and recall of 70.06%. This project will open doors to more applications of this detection method. More diseases other than Alzheimer’s disease can utilize artificial neural networks (ANN) to detect diseases early on so that lives can be restored and saved