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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is mostly prevalent in people older than 65 years. The hippocampus is a widely studied region of interest (ROI) for a number of reasons, such as memory function analysis, stress development observation and neurological disorder investigation. Moreover, hippocampal volume atro...
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Microtubule-associated protein (MAP) tau is a cross-linking molecule that provides structural stability to axonal microtubules (MT). It is considered a potential biomarker for Alzheimer's disease (AD), dementia, and other neurological disorders. It is also a signature protein for Traumatic Brain Injury (TBI) assessment. In the case of TBI, extreme...
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... Recently, thorough recommendations for diagnosing AD patients with MCI have been proposed, which might have a significant impact on early treatment and delay the onset of the disease. (2) AD individuals with MCI are expected to have cognitive deterioration (memory lapses) without having a substantial effect on their daily lives. Two types of medical variations are common in people with MCI. ...
The chance of developing "Alzheimer's Disease (AD)" increases every 5 years after 65 years of age, making it a particularly common form of neurodegenerative disorder among the older population. The use of "Magnetic Resonance Imaging (MRI)" to diagnose AD has grown in popularity in recent years. A further benefit of MRI is that it provides excellent contrast and exquisite structural detail. As a result, some studies have used biological markers backed by "structural MRI (sMRI)" to separate AD populations, which indicate differences in brain tissue size and degradation of the nervous system. The lack of properly segmented regions and essential features by the existing models might affect classification accuracy for AD. The categorization of AD in this study is based on sMRI. In this research, the hybrid Deep-Learning Models "SegNet and ResNet (SegResNet)" have been proposed for segmentation, feature extraction, and to classify the AD. SegNet network is used to identify and segment specific brain areas. Edges and circles are the SegNet's first levels, whereas the deeper layers acquire more nuanced and useful features. SegNet's last deconvolution layer produces a wide range of segmented images linked to the 3 categorization labels "Cognitive Normal (CN)", "Mild Cognitive Impairment (MCI)", and "AD" which the machine has earlier found out. To increase classification performance, the attributes of each segmented sMRI image serve as strong features of the labels. To enhance the feature information used for classification, a feature vector is built by combining the values of the pixel intensity of the segmented sMRI images. ResNet-101 classifiers are then used for characterizing vectors to identify the presence or absence of AD or MCI in each sMRI image. In terms of detection and classification accuracy, the proposed SegResNet Model is superior to the existing KNN, EFKNN, AANFIS, and ACS approaches. RESUMEN La probabilidad de desarrollar la "enfermedad de Alzheimer (EA)" aumenta cada 5 años a partir de los 65, lo que la convierte en una forma especialmente frecuente de trastorno neurodegenerativo entre la población de edad avanzada. En los últimos años se ha popularizado el uso de la resonancia magnética (RM) para diagnosticar la EA. Otra ventaja de la RM es que proporciona un contraste excelente y un detalle estructural exquisito. Como resultado, algunos estudios han utilizado marcadores biológicos respaldados por la "RM estructural (sMRI)" para separar poblaciones de EA, que indican diferencias en el tamaño del tejido cerebral y la degradación del sistema nervioso. La falta de regiones correctamente segmentadas y de características esenciales por parte de los modelos existentes podría afectar a la precisión de la clasificación de la EA. La © 2024; Los autores. Este es un artículo en acceso abierto, distribuido bajo los términos de una licencia Creative Commons (https:// creativecommons.org/licenses/by/4.0) que permite el uso, distribución y reproducción en cualquier medio siempre que la obra original sea correctamente citada Category: Health Sciences and Medicine https://doi.org/10.56294/sctconf2024651 categorización de la EA en este estudio se basa en sMRI. En esta investigación, se han propuesto los modelos híbridos de aprendizaje profundo "SegNet y ResNet (SegResNet)" para la segmentación, la extracción de características y la clasificación de la EA. La red SegNet se utiliza para identificar y segmentar áreas cerebrales específicas. Los bordes y los círculos son los primeros niveles de SegNet, mientras que las capas más profundas adquieren características más matizadas y útiles. La última capa de deconvolución de SegNet produce una amplia gama de imágenes segmentadas vinculadas a las 3 etiquetas de categorización "Cognitive Normal (CN)", "Mild Cognitive Impairment (MCI)" y "AD" que la máquina ha averiguado previamente. Para aumentar el rendimiento de la clasificación, los atributos de cada imagen sMRI segmentada sirven como características fuertes de las etiquetas. Para mejorar la información de características utilizada para la clasificación, se construye un vector de características combinando los valores de la intensidad de los píxeles de las imágenes sMRI segmentadas. A continuación, se utilizan clasificadores ResNet-101 para vectores de características con el fin de identificar la presencia o ausencia de EA o DCL en cada imagen sMRI. En términos de precisión de detección y clasificación, el modelo SegResNet propuesto es superior a los enfoques KNN, EFKNN, AANFIS y ACS existentes. Palabras clave: Enfermedad de Alzheimer; sMRI; Deep Learning; SegNet; ResNet. INTRODUCTION After the age of 65, the number of persons afflicted by ADs is expected to increase every five years, according to the Alzheimer's Association. According to recent research, AD will impact one in every 85 individuals by the year 2050. The half-life recovery period for AD varies from three to ten years, depending on the patient's age at the time of diagnosis. (1) AD patients who were diagnosed in their late 60s or early 70s had a median life span of between seven and ten years. Diagnosed AD patients' life expectancy is expected to fall by three years when they are in their 90s. Recently, thorough recommendations for diagnosing AD patients with MCI have been proposed, which might have a significant impact on early treatment and delay the onset of the disease. (2) AD individuals with MCI are expected to have cognitive deterioration (memory lapses) without having a substantial effect on their daily lives. Two types of medical variations are common in people with MCI. There are two types of MCI patients: those that go from MCI to AD are referred to as MCI converters (mAD), and those who don't go from MCI to AD are referred to as stable MCI (aAD). (3) In three years, 35 to 100 MCI patients may become dementia or AD patients, with an annual conversion rate of 5 to 10 percent. Individuals with mild amnesia and those with severe amnesia are both discriminated against. The more severe type of amnesia requires a lower normalized value on memory test results than the milder form, which requires a lower normalized value on amnesic test findings. (4) There will be modest verbal memory impairment in those who have aAD in comparison to those with mAD, which is seen as an early commencement of the impairment, which is ideal for adjusting any treatment for the condition. Variations in biomarker abnormalities, clinical history, and health responses may all affect the two MCI subtypes. For people with MCI, there is no standard treatment method due to the wide range of medical examinations and underlying causes. (5) As a result, the clinical history of MCI patients varies far from usual. However, several variables may lead to MCI, but not all of them can lead to the accompanying degeneration of the brain. To make a correct diagnosis, it is vital to recognize MCI patients with AD as the underlying cause and recognize them as such for the sake of making a diagnosis. The diagnostics must be completed as quickly as possible in the case of neurological degeneration. The classification of mAD and aAD may be used to solve this problem of qualitative diagnosis. (6)
... Alzheimer's Diagnosis research mainly includes biological tracking methods, traditional ensemble learning methods and deep learning methods. Among them, biological tracking methods are a type of method that uses traceable biomarkers to monitor the pathological process of AD, mainly including genetic AD biomarkers [5] and biochemical AD biomarkers [6] and neuroimaging AD biomarkers [7] etc. The degenerative process of AD is monitored by biological monitoring approaches, which use traceable biomarkers. ...
Most classification models for Alzheimer's Diagnosis (AD) do not have specific strategies for individual input samples, leading to the problem of easily overlooking personalized differences between samples. This research introduces a customized dynamically ensemble convolution neural network (PDECNN), which is able to build a specific integration strategy based on the distinctiveness of the sample. In this paper, we propose a personalized dynamic ensemble alzheimer's Diagnosis classification model. This model will dynamically modify the deteriorated brain areas of interest depending on various samples since it can adjust to variations in the degeneration of sample brain areas. In clinical problems, the PDECNN model has additional diagnostic importance since it can identify sample-specific degraded brain areas based on input samples. This model considers the variability of brain region degeneration levels between input samples, evaluates the degree of degeneration of specific brain regions using an attention mechanism, and selects and integrates brain region features based on the degree of degeneration. Furthermore, by redesigning the classification accuracy performance, we respectively improve it by 4%, 11%, and 8%. Moreover, the degraded brain regions identified by the model show high consistency with the clinical manifestations of AD.
... Abrol et al. [30] evaluated their 3D-CNN on a variety of binary and multi-class applications. They constructed a small test set and a cross-validation (CV) training set using ADNI information. ...
In neuropathological diseases such as Alzheimer's disease (AD), neuroimaging and magnetic resonance imaging (MRI) play a crucial role in the realm of artificial intelligence of medical things (AIoMT) by leveraging edge intelligence resources. However, accurately classifying MRI scans based on neurodegenerative diseases faces challenges due to significant variability across classes and limited intra-class differences. To address this challenge, we propose a novel approach aimed at improving the early detection of AD through MRI imaging. This method integrates a convolutional neural network (CNN) with a cascade attention model (CAM-CNN). The CAM-CNN model outperforms traditional CNNs in AD classification accuracy and processing complexity. In this architecture, the attention mechanism is effectively implemented by utilizing two constraint cost functions and a cross-network with diverse pre-trained parameters for a two-stream architecture. Additionally, two new cost functions, Satisfied Rank loss and Cross-network Similarity loss, are introduced to enhance collaboration and overall network performance. Finally, a unique entropy addition method is employed in the attention module for network integration, converting intermediate outcomes into the final prediction. These components are designed to work collaboratively and can be sequentially trained for optimal performance, thereby enhancing the effectiveness of AD stage classification and robustness to interference from MR Images. Validation using the Kaggle dataset demonstrates the model's accuracy of 99.07% in multiclass classification, ensuring precise classification and early detection of all AD subtypes. Further validation across three feature categories with varying numbers confirms the robustness of the proposed approach, with deviations from the standard criteria of less than 1%. Applied in Alzheimer's patient care, this capability holds promise for enhancing value-based therapy and clinical decision-making. It aids in differentiating Alzheimer's patients from healthy individuals, thereby improving patient care and enabling more targeted therapies.
... Recently, thorough recommendations for diagnosing AD patients with MCI have been proposed, which might have a significant impact on early treatment and delay the onset of the disease. (2) AD individuals with MCI are expected to have cognitive deterioration (memory lapses) without having a substantial effect on their daily lives. Two types of medical variations are common in people with MCI. ...
The chance of developing "Alzheimer's Disease (AD)" increases every 5 years after 65 years of age, making it a particularly common form of neurodegenerative disorder among the older population. The use of "Magnetic Resonance Imaging (MRI)" to diagnose AD has grown in popularity in recent years. A further benefit of MRI is that it provides excellent contrast and exquisite structural detail. As a result, some studies have used biological markers backed by "structural MRI (sMRI)" to separate AD populations, which indicate differences in brain tissue size and degradation of the nervous system. The lack of properly segmented regions and essential features by the existing models might affect classification accuracy for AD. The categorization of AD in this study is based on sMRI. In this research, the hybrid Deep-Learning Models "SegNet and ResNet (SegResNet)" have been proposed for segmentation, feature extraction, and to classify the AD. SegNet network is used to identify and segment specific brain areas. Edges and circles are the SegNet's first levels, whereas the deeper layers acquire more nuanced and useful features. SegNet's last deconvolution layer produces a wide range of segmented images linked to the 3 categorization labels "Cognitive Normal (CN)", "Mild Cognitive Impairment (MCI)", and "AD" which the machine has earlier found out. To increase classification performance, the attributes of each segmented sMRI image serve as strong features of the labels. To enhance the feature information used for classification, a feature vector is built by combining the values of the pixel intensity of the segmented sMRI images. ResNet-101 classifiers are then used for characterizing vectors to identify the presence or absence of AD or MCI in each sMRI image. In terms of detection and classification accuracy, the proposed SegResNet Model is superior to the existing KNN, EFKNN, AANFIS, and ACS approaches. RESUMEN La probabilidad de desarrollar la "enfermedad de Alzheimer (EA)" aumenta cada 5 años a partir de los 65, lo que la convierte en una forma especialmente frecuente de trastorno neurodegenerativo entre la población de edad avanzada. En los últimos años se ha popularizado el uso de la resonancia magnética (RM) para diagnosticar la EA. Otra ventaja de la RM es que proporciona un contraste excelente y un detalle estructural exquisito. Como resultado, algunos estudios han utilizado marcadores biológicos respaldados por la "RM estructural (sMRI)" para separar poblaciones de EA, que indican diferencias en el tamaño del tejido cerebral y la degradación del sistema nervioso. La falta de regiones correctamente segmentadas y de características esenciales por parte de los modelos existentes podría afectar a la precisión de la clasificación de la EA. La © 2024; Los autores. Este es un artículo en acceso abierto, distribuido bajo los términos de una licencia Creative Commons (https:// creativecommons.org/licenses/by/4.0) que permite el uso, distribución y reproducción en cualquier medio siempre que la obra original sea correctamente citada Category: Health Sciences and Medicine https://doi.org/10.56294/sctconf2024651 categorización de la EA en este estudio se basa en sMRI. En esta investigación, se han propuesto los modelos híbridos de aprendizaje profundo "SegNet y ResNet (SegResNet)" para la segmentación, la extracción de características y la clasificación de la EA. La red SegNet se utiliza para identificar y segmentar áreas cerebrales específicas. Los bordes y los círculos son los primeros niveles de SegNet, mientras que las capas más profundas adquieren características más matizadas y útiles. La última capa de deconvolución de SegNet produce una amplia gama de imágenes segmentadas vinculadas a las 3 etiquetas de categorización "Cognitive Normal (CN)", "Mild Cognitive Impairment (MCI)" y "AD" que la máquina ha averiguado previamente. Para aumentar el rendimiento de la clasificación, los atributos de cada imagen sMRI segmentada sirven como características fuertes de las etiquetas. Para mejorar la información de características utilizada para la clasificación, se construye un vector de características combinando los valores de la intensidad de los píxeles de las imágenes sMRI segmentadas. A continuación, se utilizan clasificadores ResNet-101 para vectores de características con el fin de identificar la presencia o ausencia de EA o DCL en cada imagen sMRI. En términos de precisión de detección y clasificación, el modelo SegResNet propuesto es superior a los enfoques KNN, EFKNN, AANFIS y ACS existentes. Palabras clave: Enfermedad de Alzheimer; sMRI; Deep Learning; SegNet; ResNet. INTRODUCTION After the age of 65, the number of persons afflicted by ADs is expected to increase every five years, according to the Alzheimer's Association. According to recent research, AD will impact one in every 85 individuals by the year 2050. The half-life recovery period for AD varies from three to ten years, depending on the patient's age at the time of diagnosis. (1) AD patients who were diagnosed in their late 60s or early 70s had a median life span of between seven and ten years. Diagnosed AD patients' life expectancy is expected to fall by three years when they are in their 90s. Recently, thorough recommendations for diagnosing AD patients with MCI have been proposed, which might have a significant impact on early treatment and delay the onset of the disease. (2) AD individuals with MCI are expected to have cognitive deterioration (memory lapses) without having a substantial effect on their daily lives. Two types of medical variations are common in people with MCI. There are two types of MCI patients: those that go from MCI to AD are referred to as MCI converters (mAD), and those who don't go from MCI to AD are referred to as stable MCI (aAD). (3) In three years, 35 to 100 MCI patients may become dementia or AD patients, with an annual conversion rate of 5 to 10 percent. Individuals with mild amnesia and those with severe amnesia are both discriminated against. The more severe type of amnesia requires a lower normalized value on memory test results than the milder form, which requires a lower normalized value on amnesic test findings. (4) There will be modest verbal memory impairment in those who have aAD in comparison to those with mAD, which is seen as an early commencement of the impairment, which is ideal for adjusting any treatment for the condition. Variations in biomarker abnormalities, clinical history, and health responses may all affect the two MCI subtypes. For people with MCI, there is no standard treatment method due to the wide range of medical examinations and underlying causes. (5) As a result, the clinical history of MCI patients varies far from usual. However, several variables may lead to MCI, but not all of them can lead to the accompanying degeneration of the brain. To make a correct diagnosis, it is vital to recognize MCI patients with AD as the underlying cause and recognize them as such for the sake of making a diagnosis. The diagnostics must be completed as quickly as possible in the case of neurological degeneration. The classification of mAD and aAD may be used to solve this problem of qualitative diagnosis. (6)
... ; https://doi.org/10.1101/2024.05.15.24307407 doi: medRxiv preprint using the "RandomizedSearchCV" function to enhance model performance. The trained model underwent a comprehensive performance evaluation using various statistical metrics, e.g., accuracy, precision, recall, F1-score, ROC AUC, and Matthews Correlation Coefficient (MCC) (29,30,1). Further, all the classifier models were tested on a test dataset to select the top-performing model. ...
Alzheimer’s disease (AD) is acknowledged as one of the most common types of dementia. Various brain regions were found to associated with AD pathology. Precuneus and fusiform gyrus are two notable regions whose role has been implicated in cognitive function. However, a thorough investigation was lacking to link these regions with AD pathology. In this study, we conducted a comprehensive radiomic based investigation using magnetic resonance imaging (MRI) scans to link precuneus and fusiform gyrus with AD pathology. We obtained T1 weighted MR scans of AD (n=133), MCI (n=311) and CN (n=195) subjects from ADNI database at three different time points (i.e., 0, 6 and 12 months). Then, we conducted statistical analysis to compare these features among AD, MCI and CN subjects. We found significant decline in gray matter volume (GMV) and cortical thickness of both precuneus and fusiform gyrus in AD as compared to the MCI and CN subjects. Further, we utilized these features to develop machine learning classifiers to classify AD from MCI and CN subjects and achieved accuracy of 97.78% and 94.41% respectively. These results strengthen the connection of precuneus and fusiform gyrus with AD pathology and opens a new avenue of AD research.
... Summary of prediction and classification methods of epilepsy, dementia, and AD in the previous studies Acc accuracy, Sen sensitivity, Pre precision, AUC area under curve, Spe specificity, ROC region of interest The authors in[44] a technique for identifying Alzheimer's disease by analyzing structural MRI (SMRI) data for differences in volume between the left and right hippocampi slice by slice. The suggested technique combines a CNN and DNNs model. ...
Brains are complex. This organ stores our knowledge, interprets our senses, moves our bodies, and governs our thoughts, emotions, memories, vision, touch, respiration, hunger, body temperature, motor skills, and every other bodily action. To help doctors, we studied and reviewed the most frequent and difficult disorders. The study will focus on using artificial intelligence to diagnose epilepsy, dementia, Parkinson's disease, and brain tumors. In this paper, we demonstrated how AI approaches can be used in the diagnostic process for a number of prevalent brain diseases and disorders. These include brain tumors, epilepsy, and dementia, particularly the Alzheimer's disease stage and Parkinson's disease. The most common dataset sources used in brain research, brain imaging modalities, and neuropsychological tests are then described and separated into open-access and private dataset categories. The most popular performance measures are discussed at the end of this work.
... The classification method using CNN can also detect diseases in humans and plants that have a high level of accuracy [22], [23], [24], [25] In addition, CNN or 3D CNN has a good result for the classification of cancer and Alzheimer's disease [26], [27]. Classification of facial expressions using CNN shows promising results [28], [29]. ...
Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and Sasambo batik consists of 20 songket fabric data with the same motif and color and 14 songket data with the same motif but different colors. In addition, there are 10 data points on songket fabrics with other motifs and colors. In addition, there are 5 data points on Sasambo batik fabrics with the same motif and color and 5 data points on Sasambo batik fabrics with the same motif but different colors. The training data rotates the image by 150as many as 20 photos. Testing with motifs with the same color shows that the system's success rate is 83.85%. The highest average recognition for Sasambo batik cloth is in testing motifs with the same color for data in the database at 93.66%. The CNN modeling classification results indicate that the Sasambo batik cloth can be a reference for developing songket categorization using a website platform or the Android system.
... However, barriers to obtaining better results include the unbalanced dataset and pre-processing operations like intensity normalization and skull stripping. On the same line, Basher et al. (2021) devised a method that utilizes volumetric characteristics extracted from sMRI data of the right and left hippocampi on a slice-by-slice basis for diagnosing AD. This method combines a CNN model and a DNN model. ...
Alzheimer’s disease affects around one in every nine persons among the elderly population. Being a neurodegenerative disease, its cure has not been established till date and is managed through supportive care by the health care providers. Thus, early diagnosis of this disease is a crucial step towards its treatment plan. There exist several diagnostic procedures viz., clinical, scans, biomedical, psychological, and others for the disease’s detection. Computer-aided diagnostic techniques aid in the early detection of this disease and in the past, several such mechanisms have been proposed. These techniques utilize machine learning models to develop a disease classification system. However, the focus of these systems has now gradually shifted to the newer deep learning models. In this regards, this article aims in providing a comprehensive review of the present state-of-the-art techniques as a snapshot of the last 5 years. It also summarizes various tools and datasets available for the development of the early diagnostic systems that provide fundamentals of this field to a novice researcher. Finally, we discussed the need for exploring biomarkers, identification and extraction of relevant features, trade-off between traditional machine learning and deep learning models and the essence of multimodal datasets. This enables both medical, engineering researchers and developers to address the identified gaps and develop an effective diagnostic system for the Alzheimer’s disease.
... Meanwhile, ADNI [1], [45], [47], and the Open Access Series of Imaging Studies (OASIS) [36], [37] stand out as significant contributors to advancing research in this domain. [2], [18], [27], [32], [33], [36], [39] OASIS MRI MRI images with a focus on brain tumor detection Structural MRI [5], [8], [14], [15], [17], [19], [21], [22], [24], [25], [36], [37], [38], [42], [43] MRI ADNI Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset Functional and Structural MRI, PET [1], [4], [4], [7], [16], [23], [28], [29], [30], [40], [41], [44], [45] ADNI GARD Gwangju Alzheimer's and Related Dementia (GARD) dataset Structural MRI [6], [20], [26], [34], [35], [40] GARD Investigation 4: How does the strategic optimization of hyperparameters influence the convergence and generalization capabilities of DL models intended for ADidentifying biomarkers? PS: The optimization of hyperparameters is vital in the construction of DL models for the discovery of AD biomarkers. ...
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that represents a significant and growing public health challenge. This work concisely summarizes AD, encompassing its pathophysiology, risk factors, clinical manifestations, diagnosis, treatment, and ongoing research. The main goal of managing AD is to reduce symptoms while improving the lives of those impacted. This letter has conducted a systematic review to analyze the prediction of AD using the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) guidelines. The major scientific databases such as Scopus, Web of Science (WoS), and IEEE Xplorer are explored, where 2018-2023 publications are considered. The article selection process is based on keywords like “Alzheimer’s disease,” “Brain Images,” “Deep Learning (DL),” etc. After rigorous analysis, 946 articles were extracted, and 42 were identified for final consideration. Further, several investigations based on the previous work are discussed along with its Proposed Solutions (PS). Finally, a case study on AD detection using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and AD Detection Network (ADD-NET) implementation is presented.
... Another method of pre-processing that has been commonly used in this field is normalization (Asl et al., 2018;Basaia et al., 2019;Basher et al., 2021;Fan et al., 2021;Folego et al., 2020;Guan et al., 2022;Jiang et al., 2022;Khagi et al., 2021;Liu et al., 2019;Ocasio & Duong, 2021;Raghavaiah & Varadarajan, 2021Saratxaga et al., 2021;Wang et al., 2022;Yan et al., 2022), which aims to reduce the grey (or colour) values in an image to a single set of relative grey (or colour) values. This guarantees that differences in imaging acquisition parameters across different imaging scanners do not strongly affect the further results since similar tissues show up in a consistent range of values throughout all image scans (Vadmal et al., 2020). ...
... It is also possible to use more than one of the previous approaches simultaneously, for example, ROI-based + Patch-based, Slice-based + ROI-based, or Slice-based + Patch-based. As to the first option, three works (Cui & Liu, 2019a;Li & Liu, 2019;Liu et al., 2020;Zhao et al., 2021) used In addition, article (Basher et al., 2021) tested the combination of the Patch-, Slice-, and ROI-based approaches. ...
Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative condition in the brain that affects memory, thinking, and behaviour. To overcome this problem, which according to the World Health Organization, is on the rise, creating strategies is essential to identify and predict the disease in its early stages before clinical manifestation. In addition to cognitive and mental tests, neuroimaging is promising in this field, especially in assessing brain matter loss. Therefore, computer‐aided diagnosis systems have been imposed as fundamental tools to help imaging technicians as the diagnosis becomes less subjective and time‐consuming. Thus, machine learning and deep learning (DL) techniques have come into play. In recent years, articles addressing the topic of Alzheimer's diagnosis through DL models are increasingly popular, with an exponential increase from year to year with increasingly higher accuracy values. However, the disease classification remains a challenging and progressing issue, not only in distinguishing between healthy controls and AD patients but mainly in differentiating intermediate stages such as mild cognitive impairment. Therefore, there is a need to develop more valuable and innovative techniques. This article presents an up‐to‐date systematic review of deep models to detect AD and its intermediate phase by evaluating magnetic resonance images. The DL models chosen by different authors are analysed, as well as their approaches regarding the used dataset and the data pre‐processing and analysis techniques.
