Alzheimer's Disease: A Survey

Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease

Enhancing Alzheimer's Detection Using a Multi-Modal Approach Hybrid Features Extraction Technique from MRI Images

The neurodegenerative illness Alzheimer's, which affects millions of people worldwide, poses significant obstacles to early detection and efficient treatment. The non-invasive technique of magnetic resonance imaging (MRI) has shown promise in identifying structural abnormalities in the brain linked to Alzheimer's disease. To address the complexity of Alzheimer's detection and enhance accuracy, this study proposes a novel hybrid feature extraction method that combines Convolutional Neural Networks (CNN), Local Binary Patterns (LBP), and Scale-Invariant Feature Transform (SIFT). After the feature extraction, PSO (Particle Swarm Optimization) and ABC (Ant Bee Colony) were applied for optimization. In this research, a dataset comprising MRI brain images from healthy individuals and Alzheimer's patients was curated. Preprocessing techniques were applied to enhance image quality and remove noise. The hybrid feature extraction method was then employed to extract distinctive and complementary features from the MRI images

Alzheimer Detection System Using Hybrid Deep Convolutional Neural Network

Alzheimer’s disease of the sixth leading causes of death in the United States of America is projected to grow to the third place of all causes of death for the elderly soon to cancer and heart decease. Timely detection and prevention are crucial to it. AD detection is based on multiple medical examinations which all lead to extensive multivariate heterogeneous data. This factor makes manual comparison, evaluation, and analysis hardly possible. The hereby study proposes a new approach to the detection of AD at the earliest stage hybrid deep learning algorithms. Several feature extraction and selection draw possible features. The method involves InceptionV3 and DenseNet for both pre-processing and classification tasks, while MobileNet enables data pre-processing and object detection. Experimental results with 100 epochs and 15 hidden layers show InceptionV3 has an accuracy of 98%, which outperforms other models available. The comparative analysis with other CNN models endorses the proposed method, achieving the highest performance across the board from our system

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

Staging of Alzheimer's disease based on MRI using CNN

Early detection of chronic diseases and determining the stages of damage to the patient is considered one of the most important stages of treatment, as it helps doctors take important remedial measures that help the patient recover or reduce the risk of the disease to a minimum. Alzheimer's disease is one of the neurological diseases that lead to brain atrophy, which leads to the loss of its functions. MRI images of the brain are used to detect Alzheimer's disease, but it is difficult to determine both the stages of the disease and the amount of damage in a patient using this MRI technique. In this research, we aim to detect Alzheimer's disease in addition to determining its stage based on deep learning techniques by using a classifier that uses the convolutional neural network (CNN). In the research, magnetic resonance images of the brain were used, and the hippocampus region was extracted in assessing the amount of damage because it is the most important region in diagnosing damage to the disease and reduce the amount of data entered into the neural network, our results show an accuracy of 95% in estimating brain damage. The results of the classifier used were able to determine the amount of damage according to four stages of the disease

Automated detection of Alzheimer disease using MRI images and deep neural networks- A review

Early detection of Alzheimer disease is crucial for deploying interventions and slowing the disease progression. A lot of machine learning and deep learning algorithms have been explored in the past decade with the aim of building an automated detection for Alzheimer. Advancements in data augmentation techniques and advanced deep learning architectures have opened up new frontiers in this field, and research is moving at a rapid speed. Hence, the purpose of this survey is to provide an overview of recent research on deep learning models for Alzheimer disease diagnosis. In addition to categorizing the numerous data sources, neural network architectures, and commonly used assessment measures, we also classify implementation and reproducibility. Our objective is to assist interested researchers in keeping up with the newest developments and in reproducing earlier investigations as benchmarks. In addition, we also indicate future research directions for this topic.Comment: 22 Pages, 5 Figures, 7 Table

Automated medical diagnosis of alzheimer´s disease using an Efficient Net convolutional neural network

Producción CientíficaAlzheimer's disease (AD) poses an enormous challenge to modern healthcare. Since 2017, researchers have been using deep learning (DL) models for the early detection of AD using neuroimaging biomarkers. In this paper, we implement the EfficietNet-b0 convolutional neural network (CNN) with a novel approach—"fusion of end-to-end and transfer learning"—to classify different stages of AD. 245 T1W MRI scans of cognitively normal (CN) subjects, 229 scans of AD subjects, and 229 scans of subjects with stable mild cognitive impairment (sMCI) were employed. Each scan was preprocessed using a standard pipeline. The proposed models were trained and evaluated using preprocessed scans. For the sMCI vs. AD classification task we obtained 95.29% accuracy and 95.35% area under the curve (AUC) for model training and 93.10% accuracy and 93.00% AUC for model testing. For the multiclass AD vs. CN vs. sMCI classification task we obtained 85.66% accuracy and 86% AUC for model training and 87.38% accuracy and 88.00% AUC for model testing. Based on our experimental results, we conclude that CNN-based DL models can be used to analyze complicated MRI scan features in clinical settings.Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL