Neuroscience informatics最新文献

{"title":"Automatic classification of the cerebral vascular bifurcations using dimensionality reduction and machine learning","authors":"Ibtissam Essadik ,&nbsp;Anass Nouri ,&nbsp;Raja Touahni ,&nbsp;Romain Bourcier ,&nbsp;Florent Autrusseau","doi":"10.1016/j.neuri.2022.100108","DOIUrl":"https://doi.org/10.1016/j.neuri.2022.100108","url":null,"abstract":"<div><p>This paper presents a method for the automatic labeling of vascular bifurcations along the Circle of Willis (CoW) in 3D images. Our automatic labeling process uses machine learning as well as dimensionality reduction algorithms to map selected bifurcation features to a lower dimensional space and thereafter classify them. Unlike similar studies in the literature, our main goal here is to avoid a classical registration step commonly applied before resorting to classification. In our approach, we aim to collect various geometric features of the bifurcations of interest, and thanks to dimensionality reduction, to discard the irrelevant ones before using classifiers.</p><p>In this paper, we apply the proposed method to 50 human brain vascular trees imaged via Magnetic Resonance Angiography (MRA). The constructed classifiers were evaluated using the Leave One Out Cross-Validation approach (LOOCV). The experimental results showed that the proposed method could assign correct labels to bifurcations at 96.8% with the Naive Bayes classifier. We also confirmed its functionality by presenting automatic bifurcation labels on independent images.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277252862200070X/pdfft?md5=b35eea2b6a51fcd0cc0bb4a5bc9143c1&pid=1-s2.0-S277252862200070X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136886480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multistage DPIRef-Net: An effective network for semantic segmentation of arteries and veins from retinal surface","authors":"Geetha Pavani ,&nbsp;Birendra Biswal ,&nbsp;Tapan Kumar Gandhi","doi":"10.1016/j.neuri.2022.100074","DOIUrl":"10.1016/j.neuri.2022.100074","url":null,"abstract":"<div><p>Retinal vascular changes are the early indicators for many progressive diseases like diabetes, hypertension, etc. However, the manual procedure in detecting these vascular changes is a time-consuming process and may cause a large variance, especially when dealing with a large dataset. Therefore, computer-aided diagnosis of the retinal vascular network plays a crucial role in analyzing the patients effectively with high precision. As a result, this paper presents a robust deep learning Multistage Dual-Path Interactive Refinement Network (DPIRef-Net) for segmenting the vascular maps of arteries and veins from the retinal surface. The main novelty of the proposed model lies in segmenting both the regional and edge salient feature maps that will reduce the degeneration problems of pooling and striding. This eventually preserves the edges of vascular branches and suppresses the false positive rate. In addition to this, a novel guided filtering technique is employed to segment the final accurate arteries and veins vascular networks from predicted regional and edge feature maps. The proposed Multistage DPIRef-Net is trained and tested on different benchmark datasets like DRIVE, HRF, AVRDB, INSPIRE AVR, VICAVR, and Dual-Mode datasets. The proposed model illustrated superior performance in segmenting the vascular maps on all datasets by achieving an average accuracy of 97%, a sensitivity of 96%, a specificity of 98%, and a dice coefficient of 98%.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277252862200036X/pdfft?md5=839ffed6723996f2137045b3dcb4cd99&pid=1-s2.0-S277252862200036X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44685616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors predicting effective aneurysm early obliteration after flow re-direction endoluminal device placement for unruptured intracranial cerebral aneurysms","authors":"Shinichiro Yoshida ,&nbsp;Hidetoshi Matsukawa ,&nbsp;Kousei Maruyama ,&nbsp;Yoshiaki Hama ,&nbsp;Hiroya Morita ,&nbsp;Yuichiro Ota ,&nbsp;Noriaki Tashiro ,&nbsp;Fumihiro Hiraoka ,&nbsp;Hiroto Kawano ,&nbsp;Shigetoshi Yano ,&nbsp;Hiroshi Aikawa ,&nbsp;Yoshinori Go ,&nbsp;Kiyoshi Kazekawa","doi":"10.1016/j.neuri.2022.100107","DOIUrl":"10.1016/j.neuri.2022.100107","url":null,"abstract":"<div><h3>Objective</h3><p>There have been few reports of the outcomes of flow re-direction endoluminal device (FRED) treatment for unruptured cerebral aneurysms, and patient factors associated with effective aneurysm obliteration have yet to be determined. Flow diverters also have problems with delayed rupture. The objective of this study was to investigate associations between the cases of early obliteration of aneurysm after FRED treatment and a range of factors.</p></div><div><h3>Method</h3><p>A retrospective analysis of 75 aneurysms in 72 patients whose response to treatment was evaluated by cerebral angiography 6 months after FRED treatment was conducted. The aneurysm obliteration rate was classified according to the O'Kelly-Marotta grading scale (OKM grade). The patients were classified into those assessed as OKM Grade A or Grade B with poor aneurysm obliteration (poor obliteration), and those assessed as Grade C or Grade D with good aneurysm obliteration (good obliteration). The parameters evaluated were age, sex, medical history, immediate postoperative eclipse sign, P2Y12 reaction units (PRU), aspirin reaction units (ARU), operating time, maximum aneurysm diameter measured on cerebral angiography, and aneurysm location.</p></div><div><h3>Results</h3><p>At 6 months post-treatment, 19 aneurysms (25.3%) were OKM Grade A, 15 (20%) were Grade B, 10 (13.3%) were Grade C, and 31 (41.3%) were Grade D. Age ≥67.5 years was significantly associated with a poor obliteration [odds ratio (OR): 0.1; 95% confidence interval (95%CI): 0.2-0.4; <span><math><mi>p</mi><mo>=</mo><mn>0.002</mn></math></span>] and intracranial side wall aneurysm [OR: 21.7; 95%CI: 1.6–284.5; <span><math><mi>p</mi><mo>=</mo><mn>0.01</mn></math></span>].</p></div><div><h3>Conclusions</h3><p>The results of this study demonstrated that age was associated with aneurysm obliteration after FRED treatment. This finding may be useful for further studies investigating factors predictive of the aneurysm obliteration rate and the residual aneurysm rate after FRED treatment.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000693/pdfft?md5=4364a1c16aee5f9a1a4bf0b99a800b21&pid=1-s2.0-S2772528622000693-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46332347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges","authors":"Maleeha Imtiaz ,&nbsp;Syed Afaq Ali Shah ,&nbsp;Zia ur Rehman","doi":"10.1016/j.neuri.2022.100079","DOIUrl":"10.1016/j.neuri.2022.100079","url":null,"abstract":"<div><p>Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100079"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000413/pdfft?md5=4fa14f078d8e889a1dcf1e11dfed49fb&pid=1-s2.0-S2772528622000413-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42011574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic architecture based deep learning approach for glioblastoma brain tumor survival prediction","authors":"Disha Sushant Wankhede,&nbsp;R. Selvarani","doi":"10.1016/j.neuri.2022.100062","DOIUrl":"10.1016/j.neuri.2022.100062","url":null,"abstract":"<div><p>A correct diagnosis of brain tumours is crucial to making an accurate treatment plan for patients with the disease and allowing them to live a long and healthy life. Among a few clinical imaging modalities, attractive reverberation imaging gives extra different data about the tissues. The use of MRI-Magnetic Resonance Imaging tests is a significant method for identifying disorders throughout the human body. Deep learning provides a solution for efficiently detecting Brain Tumour. The work has used MRI images for predicting the glioblastoma of brain tumours. Initially, data is retrieved from hospitals in form of an image database to continue with the brain tumour prediction. Pre-processing of dataset images is a mandatory step to enhance the accuracy and smooth line supplementary stages. The intensity value of each MRI (Magnetic Resonance Imaging) is subtracted by the mean intensity value and standard deviation of the brain region. Further, reduce the medical image noise by employing a bilateral filter. Further, the preprocessed medical images are used for extracting the radiomics features from images as well as tumour segmentation. Thus the work adopts the tumor is automatically segmented into four compartments using mutually exclusive rules using Modified Fuzzy C Means Clustering (MFCM). The clustering-based approach is very beneficial in MR tumour segmentation; it categorizes the pixels using certain radiomics features. The most important problem in the radiomics-based machine learning model is the dimension of data. Moreover, using a GWO (Grey Wolf Optimizer) with rough set theory, we propose a novel dimensionality reduction algorithm. This method is employed to find the significant features from the extracted images and differentiate HG (high-grade) and LG (Low-grade) from GBM while varying feature correlation limits were applied to remove redundant features. Finally, the article proposed the dynamic architecture of Multilevel Layer modelling in Faster R-CNN (MLL-CNN) approach based on feature weight factor and relative description model to build the selected features. This reduces the overall computation and performs long-tailed classification. This results in the development of CNN training performance more accurate. Results show that the general endurance expectation of GBM cerebrum growth with more prominent exactness of about 95% with the decreased blunder rate to be 2.3%. In the calculation of similarity between segmented tissues and ground truth, different tools produce correspondingly different predictions.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100062"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000243/pdfft?md5=233d787e01e7af6d072522ff347defc3&pid=1-s2.0-S2772528622000243-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48343185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epileptic EEG activity detection for children using entropy-based biomarkers","authors":"Sadeem Nabeel Saleem Kbah ,&nbsp;Noor Kamal Al-Qazzaz ,&nbsp;Sumai Hamad Jaafer ,&nbsp;Mohannad K. Sabir","doi":"10.1016/j.neuri.2022.100101","DOIUrl":"10.1016/j.neuri.2022.100101","url":null,"abstract":"<div><p>Seizures, which last for a while and are a symptom of epilepsy, are bouts of excessive and abnormally synchronized neuronal activity in the patient's brain. For young children, in particular, early diagnosis and treatment are essential to optimize the likelihood of the best possible child-specific result. Electroencephalogram (EEG) signals can be inspected to look for epileptic seizures. However, certain epileptic patients with severe cases show high rates of misdiagnosis or failure to notice the seizures, and they do not demonstrate any improvement in healing as a result of their inability to respond to medical treatment. The purpose of this study was to identify EEG biomarkers that may be used to distinguish between children with epilepsy and otherwise healthy and normal subjects. Savitzky-Golay (SG) filter was used to record and analyze the data from 19 EEG channels. EEG background activity was used to calculate amplitude-aware permutation entropy (AAPE) and enhanced permutation entropy (impe). The hypothesis that the irregularity and complexity in epileptic EEG were decreased in comparison with healthy control participants was tested statistically using the t-test (<em>p</em> &lt; 0,05). As a method of dimensionality reduction, principle component analysis (PCA) was used. The EEG signals of the patients with epileptic seizures were then separated from those of the control individuals using decision tree (DT) and random forest (RF) classifiers. The findings indicate that the EEG of the AAPE and impe was decreased for epileptic patients. A comparison study has been done to see how well the DT and RF classifiers work with the SG filter, AAPE and impe features, and PCA dimensionality reduction technique. When identifying patients with epilepsy and control subjects, PCA with DT and RF produced accuracies of 85% and 80%, respectively, but without the PCA, DT and RF showed accuracies of 75% and 72.5%, respectively. As a result, the EEG may be a trustworthy index for looking at short-term indicators that are sensitive to epileptic identification and classification.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000632/pdfft?md5=e56ee33f16a52bc4895dbf82c0664fcb&pid=1-s2.0-S2772528622000632-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49249075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel depth search based light weight CAR network for the segmentation of brain tumour from MR images","authors":"Sreekar Tankala ,&nbsp;Geetha Pavani ,&nbsp;Birendra Biswal ,&nbsp;G. Siddartha ,&nbsp;Gupteswar Sahu ,&nbsp;N. Bala Subrahmanyam ,&nbsp;S. Aakash","doi":"10.1016/j.neuri.2022.100105","DOIUrl":"10.1016/j.neuri.2022.100105","url":null,"abstract":"<div><p>In this modern era, brain tumour is one of the dreadful diseases that occur due to the growth of abnormal cells or by the accumulation of dead cells in the brain. If these abnormalities are not detected in the early stages, they lead to severe conditions and may cause death to the patients. With the advancement of medical imaging, Magnetic Resonance Images (MRI) are developed to analyze the patients manually. However, this manual screening is prone to errors. To overcome this, a novel depth search-based network termed light weight channel attention and residual network (LWCAR-Net) is proposed by integrating with a novel depth search block (DSB) and a CAR module. The depth search block extracts the pertinent features by performing a series of convolution operations enabling the network to restore low-level information at every stage. On other hand, CAR module in decoding path refines the feature maps to increase the representation and generalization abilities of the network. This allows the network to locate the brain tumor pixels from MRI images more precisely. The performance of the depth search based LWCAR-Net is estimated by testing on different globally available datasets like BraTs 2020 and Kaggle LGG dataset. This method achieved a sensitivity of 95%, specificity of 99%, the accuracy of 99.97%, and dice coefficient of 95% respectively. Furthermore, the proposed model outperformed the existing state-of-the-art models like U-Net++, SegNet, etc by achieving an AUC of 98% in segmenting the brain tumour cells.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277252862200067X/pdfft?md5=d58e2d75cb0f6b8b83863574cd90f066&pid=1-s2.0-S277252862200067X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47666735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CTtrack: A CNN+Transformer-based framework for fiber orientation estimation & tractography","authors":"S.M.H. Hosseini ,&nbsp;M. Hassanpour ,&nbsp;S. Masoudnia ,&nbsp;S. Iraji ,&nbsp;S. Raminfard ,&nbsp;M. Nazem-Zadeh","doi":"10.1016/j.neuri.2022.100099","DOIUrl":"https://doi.org/10.1016/j.neuri.2022.100099","url":null,"abstract":"<div><p>In recent years, multiple data-driven fiber orientation distribution function (fODF) estimation algorithms and automatic tractography pipelines have been proposed to address the limitations of traditional methods. However, these approaches lack precision and generalizability. To tackle these shortcomings, we introduce CTtrack, a CNN+Transformer-based pipeline to estimate fODFs and perform tractography. In this approach, a convolutional neural network (CNN) module is employed to project the resampled diffusion-weighted magnetic resonance imaging (DW-MRI) data to a lower dimension. Then, a transformer model estimates the fiber orientation distribution functions using the projected data within a local block around each voxel. The proposed model represents the extracted fODFs by spherical harmonics coefficients. The predicted fiber ODFs can be used for both deterministic and probabilistic tractography. Our pipeline was tested in terms of the precision and robustness in estimating fODFs and performing tractography using both simulated and real diffusion data. The Tractometer tool was employed to compare our method with the classical and data-driven tractography approaches. The qualitative and quantitative assessments illustrate the competitive performance of our framework compared to other available algorithms.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100099"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000619/pdfft?md5=55e258b2643f452c1045044d358bbfac&pid=1-s2.0-S2772528622000619-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136886483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective connectivity in individuals with Alzheimer's disease and mild cognitive impairment: A systematic review","authors":"Sayedeh-Zahra Kazemi-Harikandei ,&nbsp;Parnian Shobeiri ,&nbsp;Mohammad-Reza Salmani Jelodar ,&nbsp;Seyed Mohammad Tavangar","doi":"10.1016/j.neuri.2022.100104","DOIUrl":"10.1016/j.neuri.2022.100104","url":null,"abstract":"<div><h3>Background</h3><p>Alzheimer's disease (AD) is the most common cause of dementia. Effective connectivity (EC) methods signify the direction of brain interactions. The identified inter-system mappings can be helpful in characterizing the pathophysiology of the disease.</p></div><div><h3>Methods and Results</h3><p>We conducted a systematic review of the alterations in EC findings in individuals with AD or Mild Cognitive Impairment (MCI) from PubMed, Scopus, and Google Scholar from fMRI studies. We extracted EC alterations and altered network findings related to specific cognitive impairments. Additionally, we brought a narrative synthesis on the clinical-pathologic relevance of the utilized computational methods. Thirty-nine studies retrieved from the full-text screening. A general pattern of disconnection in several hub centers and changes in inter-network interactions was identified.</p></div><div><h3>Conclusion</h3><p>In summary, this study demonstrated the beneficial role of EC analyses and network measures in understanding the pathophysiology of AD. Future studies are needed to bring out methodologically consistent data for more structured meta-analytic views.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000668/pdfft?md5=4b2fd13b687332fa0a4be378f10b8576&pid=1-s2.0-S2772528622000668-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48591440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alzheimer's disease detection from structural MRI using conditional deep triplet network","authors":"Maysam Orouskhani ,&nbsp;Chengcheng Zhu ,&nbsp;Sahar Rostamian ,&nbsp;Firoozeh Shomal Zadeh ,&nbsp;Mehrzad Shafiei ,&nbsp;Yasin Orouskhani","doi":"10.1016/j.neuri.2022.100066","DOIUrl":"10.1016/j.neuri.2022.100066","url":null,"abstract":"<div><p>Alzheimer's disease (AD) as an advanced brain disorder may cause damage to the memory and tissue loss in the brain. Since AD is a mostly costly disease, various deep learning-based models have been presented to achieve a high accuracy classifier to Alzheimer's diagnosis. While a model with high discriminative features is required to obtain a high performance classifier, the lack of image samples in the datasets causes over-fitting and deteriorates the performance of deep learning models. To overcome this problem, few-shot learning such as deep metric learning was introduced. In this paper we employ a novel deep triplet network as a metric learning approach to brain MRI analysis and Alzheimer's detection. The proposed deep triplet network uses a conditional loss function to overcome the lack of limited samples and improve the accuracy of the model. The basic network of this model inspired by VGG16 and experiments are conducted on open access series of imaging studies (OASIS). The experiments show that the proposed model outperforms the state-of-the-art models in term of accuracy.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"2 4","pages":"Article 100066"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772528622000280/pdfft?md5=a33dd20b94a47f6279fceee89b77c2e5&pid=1-s2.0-S2772528622000280-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48659461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}