Prediction and classification of Alzheimer Disease categories using Integrated Deep Transfer Learning Approach

Alzheimer's disease (AD) is a type of dementia that affects thinking, behavior, and memory. Eventually, symptoms become serious enough to interfere with daily activities of a patient. Older people are typically affected by AD. The complexity of the brain's structure and functioning makes early AD diagnosis difficult, despite the fact that research on the disease has grown significantly. Inadequate datasets on AD are a major barrier to furthering research into the disease's diagnosis. On the other hand, stages of AD classification remain a challenge to this date for the Deep Learning (DL)-based techniques previously employed in many studies. This study solves the challenge of insufficient data on the AD dataset and the poor accuracy of multi-class stage diagnosis by proposing a DL framework based on a transfer learning approach. A total of five different transfer learning models were trained, and from the evaluation, it has been clear that almost all the models performed greatly compared to the best performing model that was proposed, which was Inception V3with an overall accuracy of 98.58%.

Implementing Deep Learning for comprehensive aircraft icing and actuator/sensor fault detection/identification

BackgroundWith the global change of demographics towards an increase of the elderly population, diseases related to some form of dementia have become more common in the 21st century. Leading the hall of dementia problems is Alzheimer's Disease (AD), responsible for more than 50% of the reported cases. It is also known that early diagnosis helps to lead to a better prognosis. So, this review aims to evaluate the role of disponible neuropsychological tests (NT) on early diagnosis of AD, using the available literature, in order to determine the importance of its use.MethodAn active search was performed in the PubMed database, using the keywords “Alzheimer's disease”, “early diagnosis”, “neuropsychological” and their equivalents. All articles published from 1998 to 2024 in any language were selected. The title and abstract were analyzed, duplicate results were eliminated and all studies that had the evaluation of the use of NT on early diagnosis of AD as their objective were included. Studies that analyzed only other types of dementia or non‐psychological tests were excluded.ResultThe search yielded 77 articles, with 9 meeting the inclusion criteria. Findings reveal that various NTs show promise for early AD diagnosis, with tools like the Brain Health Assessment‐Cognitive Score and the DMS48 visual recognition memory test effectively identifying early cognitive impairment. The DMS48, in particular, highlighted that mild cognitive impairment patients with visual memory deficits face a faster cognitive decline, indicating a higher AD progression risk. The visual oddity detection task also proved effective, and combining the Mini‐Mental State Examination with other NTs, such as the Addenbrooke's Cognitive Examination, in screening was especially useful. Additionally, verbal fluency tests have shown several impairments of the early phase of AD and culturally adaptable tools like the Cross‐Cultural Neuropsychological Test Battery offered reliable assessment across diverse populations.ConclusionThe literature on the use of NT on early diagnosis of AD lacks data, no randomized clinical trial or large analytical observational study was found. However, among the information analyzed, the results suggest that combining the neuropsychological measures alongside biomarkers and informant‐based assessments enhances early AD detection and diagnostic accuracy.

Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task. A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively. MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.

BackgroundThe link between late‐life depression and Alzheimer’s disease (AD) has long been investigated, however it is still unclear whether depression acts as a risk factor, a prodromal feature or a comorbid condition in AD. Clarifying the nature of this link has important implications in the early intervention and diagnosis of AD. The relatively recent introduction of in‐vivo brain biomarkers for AD pathology offers the chance to explore the relationship between depression and pathologically‐confirmed neurodegeneration. Here, we examined the association among objective cognitive functioning, self‐reported cognitive failures and AD pathology in patients with late‐life depression.MethodSixty‐three older adults with no formal diagnosis of cognitive impairment underwent a comprehensive cognitive assessment and Amyloid‐PET Imaging at the Imperial Memory Unit. Participants were classified into two groups, according to the presence (n=33, mean age=71.82 ±4.98) or absence (n=30, mean age=71.30 ±5.80) of late‐life depression. Late‐life depression was confirmed through validated questionnaires (GDS ≥ 6 and/or HDRS ≥ 12) and medical history review. Amyloid‐PET images were qualitatively read as amyloid positive or amyloid negative by two experienced nuclear medicine radiologists (NMRs). Equivocal scans (6%) were read by a third independent NMR. Cognitive data in the two groups were compared using non‐parametric tests.ResultsAmyloid status did not differ significantly between the two groups (p=.367), with 3 (9%) depressed and 5 (17%) non‐depressed participants reported as amyloid‐positive. Despite higher levels of self‐reported cognitive failures (p<.001), the depressed group did not differ from the non‐depressed group in a global measure of cognition (MMSE depressed: 28.09±1.69; non‐depressed: 28.93±4.66) and showed worse performance in 4 (Word List short‐delay recall, Digit Span backward and forward, Coding) of the 17 cognitive measures administered. Follow up data, currently being collected at our Unit, will add information about the long‐term trajectory of cognition in late life depression.ConclusionsIn our sample, depressive symptoms were associated with a limited number of cognitive measures but not with AD pathology. This suggests that depression seems to have a limited role in the early diagnosis of AD. Future studies should examine the role of specific features of depression and of other widespread neuropsychiatric symptoms (e.g., anxiety) in AD.

Objective.Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine learning or deep learning have been proposed to objectively distinguish between MDD and healthy individuals. However, most current methods detect depression based on multichannel EEG signals, which constrains its application in daily life. The context in which EEG is obtained can vary in terms of study designs and EEG equipment settings, and the available depression EEG data is limited, which could also potentially lessen the efficacy of the model in differentiating between MDD and healthy subjects. To solve the above challenges, a depression detection model leveraging transfer learning with the single-channel EEG is advanced.Approach.We utilized a pretrained ResNet152V2 network to which a flattening layer and dense layer were appended. The method of feature extraction was applied, meaning that all layers within ResNet152V2 were frozen and only the parameters of the newly added layers were adjustable during training. Given the superiority of deep neural networks in image processing, the temporal sequences of EEG signals are first converted into images, transforming the problem of EEG signal categorization into an image classification task. Subsequently, a cross-subject experimental strategy was adopted for model training and performance evaluation.Main results.The model was capable of precisely (approaching 100% accuracy) identifying depression in other individuals by employing single-channel EEG samples obtained from a limited number of subjects. Furthermore, the model exhibited superior performance across four publicly available depression EEG datasets, thereby demonstrating good adaptability in response to variations in EEG caused by the context.Significance.This research not only highlights the impressive potential of deep transfer learning techniques in EEG signal analysis but also paves the way for innovative technical approaches to facilitate early diagnosis of associated mental disorders in the future.

Alzheimer's disease (AD) is an incurable neurodegenerative disorder accounting for 70%-80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25AD) from Alzheimer's Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset, posing a serious threat to human physical and mental health. The cognitive impairments caused by AD are generally diffuse and overlap symptomatically with other neurodegenerative diseases. Moreover, the symptoms of AD are often covert, leading to missed opportunities for optimal treatment after diagnosis. Therefore, early diagnosis of AD is crucial. In vitro diagnostic biomarkers not only contribute to the early clinical diagnosis of AD but also aid in further understanding the disease's pathogenesis, predicting disease progression, and observing the effects of novel candidate therapeutic drugs in clinical trials. Currently, although there are numerous biomarkers associated with AD diagnosis, the complex nature of AD pathogenesis, limitations of individual biomarkers, and constraints of clinical detection methods have hindered the development of efficient, cost-effective, and convenient diagnostic methods and standards. This article provides an overview of the research progress on in vitro diagnostic biomarkers and detection methods related to AD in recent years.

Alzheimer’s disease (AD) is a progressive neurodegenerative condition that affects the elderly population. The early detection and diagnosis of AD is critical for achieving effective treatment, as it can greatly improve the patient experience. AD can be viewed through imaging techniques like MRI, PET, and SPECT, providing valuable information about structural and functional changes. These findings are important in understanding this area. However, each imaging modality offers a different perspective. This information can be better collected from several of the other modalities as well as from some others to improve accuracy and reliability in AD detection. By combining information from different imaging modalities, such as MRI, PET, DTI, and fMRI, automated multimodal medical image frameworks aim to create a fused representation that preserves the relevant features from each modality. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), among other deep learning techniques, have been prevalent in these frameworks for learning discriminative and informative features from multi-modal data. In this paper, The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is used for experimental analysis. The proposed work gives 98.94% of accuracy and 1.06% of error which is greater than the existing approaches.

The most prevalent cause of dementia in older people is Alzheimer's disease (AD). Dementia is a neurological disease that severely limits a person's capacity to do everyday tasks. The importance of early Alzheimer's disease diagnosis can be attributed to several factors. Most of them provide for Alzheimer's disease therapies that can slow down the disease's development. The CT scan reveals a degree of generalized cortical atrophy in Alzheimer's disease patients. As a result, CT scan picture processing is critical in the early detection of Alzheimer's disease. Here, image processing is used to detect the objects in CT pictures. Edge detection is a critical first phase in image processing since it defines the discontinuities in gray-level images. The majority of them are clinic-based structural MRI images with small sample size and few scanning layers. Deep learning, on the other hand, necessitates a large amount of annotated details. This paper suggests a dataset increment approach based on a weighted mixture of positive and negative tests and a learning method with a limited number of samples to meet the realistic requirements of clinical evaluation of Alzheimer's disease. It produces a GKFCM Clustering model that can collect more image feature details and boost the model's generalization ability.

Tuberculosis (TB) remains a leading infectious disease worldwide, and early, reliable screening using chest X-rays (CXRs) is essential in low-resource settings. The scarcity of labeled TB-positive CXR images limits the effectiveness of deep learning models. This study investigates whether Conditional Generative Adversarial Networks (CGANs) can generate realistic TB-positive CXR images to balance training data and improve the classification performance of fine-tuned deep transfer learning (DTL) models. We trained a CGAN (LSGAN formulation) to synthesize class-conditional grayscale CXR images at 128x128 resolution and used the generated images to augment the Shenzhen TB dataset. Three pre-trained DTL architectures (DenseNet121, VGG16, and MobileNetV3Small) were fine-tuned on both original and CGAN-augmented datasets. Experiments used stratified 70/10/20 train/validation/test splits and a fixed random seed (random_state=42) to ensure reproducibility. Model performance was evaluated using accuracy, precision, recall (sensitivity), F1-score, confusion matrices, and ROC/AUC curves. The experiments were executed on an NVIDIA Tesla P100 GPU (16GB) in a Kaggle runtime environment; total CGAN+classifier processing reported a wall-clock runtime of 39 minutes 30 seconds for the baseline experimental run. CGAN augmentation produced consistent improvements across models: DenseNet121 improved from 93.0% to 94.6% test accuracy, VGG16 improved from 96.3% to 96.8%, and MobileNetV3Small improved from 93.0% to 93.5%. Class-conditional GAN augmentation can modestly but usefully improve DTL classifier performance in TB detection when labeled data are scarce, though further cross-dataset validation is required before clinical deployment.

Alzheimer's Disease (AD) is a serious neurological illness that causes memory loss gradually by destroying brain cells. This deadly brain illness primarily strikes the elderly, impairing their cognitive and bodily abilities until brain shrinkage occurs. Modern techniques are required for an accurate diagnosis of AD. Machine learning has gained attraction in the medical field as a means of determining a person's risk of developing AD in its early stages. One of the most advanced soft computing neural network-based Deep Learning (DL) methodologies has garnered significant interest among researchers in automating early-stage AD diagnosis. Hence, a comprehensive review is necessary to gain insights into DL techniques for the advancement of more effective methods for diagnosing AD. This review explores multiple biomarkers associated with Alzheimer's Disease (AD) and various DL methodologies, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), The k-nearest-neighbor (k-NN), Deep Boltzmann Machines (DBM), and Deep Belief Networks (DBN), which have been employed for automating the early diagnosis of AD. Moreover, the unique contributions of this review include the classification of ATN biomarkers for Alzheimer's Disease (AD), systemic description of diverse DL algorithms for early AD assessment, along with a discussion of widely utilized online datasets such as ADNI, OASIS, etc. Additionally, this review provides perspectives on future trends derived from critical evaluation of each variant of DL techniques across different modalities, dataset sources, AUC values, and accuracies.

Improving Alzheimer’s disease classification using novel rewards in deep reinforcement learning

Handwriting and hand drawings have historically been used as a proxy metric to evaluate psychological and cognitive traits, in addition to fine motor skills. Detecting onset of cognitive diseases early is a very challenging task due to the expertise required to evaluate each individual subject, as well as the time that the process entails. In this work, we evaluated the application of state-of-the-art deep learning and transfer learning models to determine if the author of a copied hand drawing of a template displays signs of cognitive disease. Compared to expert cognitive evaluation, our best performing method yielded a mean accuracy of 67.60% and area under ROC of 0.595 in determining if an undiagnosed subject displays signs of cognitive disease. Our results suggest that state of the art techniques in deep learning have the potential to help alleviate the difficulty of screening for early signs of cognitive disease. Results also suggest that transfer learning did not perform as well as a purpose-built network architecture trained from scratch. Lastly, our results indicate that models which consider all drawings made by a subject outperform models that look at drawing-evaluation pairs independently.

Background: The VALCODIS (Valencian Cognitive Diseases Study) cohort was designed and studied at the Hospital Universitari i Politècnic La Fe (Valencia, Spain) for the research of cognitive diseases, especially in the search for new biomarkers of Alzheimer's disease (AD). Methods: Participants in the VALCODIS cohort had cerebrospinal fluid (CSF) and blood samples, neuroimaging, and neuropsychological tests. The ApoE genotype was evaluated to identify its relationship with CSF biomarkers and neuropsychological tests in AD and non-AD participants. Results: A total of 1249 participants were included. They were mainly AD patients (n = 547) but also patients with other dementias (frontotemporal lobar dementia (n = 61), Lewy body dementia without AD CSF signature (n = 10), vascular dementia (n = 24) and other specific causes of cognitive impairment (n = 442), and patients with subjective memory complaints (n = 165)). In the ApoE genotype evaluation, significant differences were found for Aβ42 levels between genotypes in both AD and non-AD patients, as well as a negative correlation between tau values and a cognitive test in non-carriers and ε4 heterozygous. Conclusions: The VALCODIS cohort provides biologically diagnosed patients with demographical, clinical and biochemical data, and biological samples for further studies on early AD diagnosis. Also, the ApoE genotype evaluation showed correlations between CSF biomarkers and neuropsychological tests.