Diagnosis of Alzheimer's disease with Sobolev gradient‐based optimization and 3D convolutional neural network

Göçeri, Evgin

doi:10.1002/cnm.3225

Cited by 94 publications

(36 citation statements)

References 61 publications

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“…The advantage of the proposed algorithm is the ability to strengthen edges at a substantially equal distance from the central point. Although Sobolev gradient-based optimisers have been used in some previous studies [31–33], the method proposed in this work efficiently uses the traditional optimiser. In addition, the proposed approach is suitable for detecting weak and round curves in a noisy background since it provides successful results without an extra step for noise reduction or intensity normalisation, as seen in previous studies [34, 35].…”

Section: Methodsmentioning

confidence: 99%

Towards the automation of early-stage human embryo development detection

Raudonis

Paulauskaitė-Tarasevičienė

Šutienė

et al. 2019

BioMed Eng OnLine

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BackgroundInfertility and subfertility affect a significant proportion of humanity. Assisted reproductive technology has been proven capable of alleviating infertility issues. In vitro fertilisation is one such option whose success is highly dependent on the selection of a high-quality embryo for transfer. This is typically done manually by analysing embryos under a microscope. However, evidence has shown that the success rate of manual selection remains low. The use of new incubators with integrated time-lapse imaging system is providing new possibilities for embryo assessment. As such, we address this problem by proposing an approach based on deep learning for automated embryo quality evaluation through the analysis of time-lapse images. Automatic embryo detection is complicated by the topological changes of a tracked object. Moreover, the algorithm should process a large number of image files of different qualities in a reasonable amount of time.MethodsWe propose an automated approach to detect human embryo development stages during incubation and to highlight embryos with abnormal behaviour by focusing on five different stages. This method encompasses two major steps. First, the location of an embryo in the image is detected by employing a Haar feature-based cascade classifier and leveraging the radiating lines. Then, a multi-class prediction model is developed to identify a total cell number in the embryo using the technique of deep learning.ResultsThe experimental results demonstrate that the proposed method achieves an accuracy of at least 90% in the detection of embryo location. The implemented deep learning approach to identify the early stages of embryo development resulted in an overall accuracy of over 92% using the selected architectures of convolutional neural networks. The most problematic stage was the 3-cell stage, presumably due to its short duration during development.ConclusionThis research contributes to the field by proposing a model to automate the monitoring of early-stage human embryo development. Unlike in other imaging fields, only a few published attempts have involved leveraging deep learning in this field. Therefore, the approach presented in this study could be used in the creation of novel algorithms integrated into the assisted reproductive technology used by embryologists.

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Section: Methodsmentioning

confidence: 99%

Towards the automation of early-stage human embryo development detection

Raudonis

Paulauskaitė-Tarasevičienė

Šutienė

et al. 2019

BioMed Eng OnLine

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“…The weights of the network were calculated using the gradient descent optimization using the Root Mean Square Propagation (rmsprop) algorithm [46]. Recently, Sobolev gradient based optimization has been used in deep network based methods to diagnose AD [47,48]. However, the standard gradient descent optimization is efficient in the proposed approach in terms of computation.…”

Section: Plos Onementioning

confidence: 99%

Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks

et al. 2020

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Machine learning algorithms are currently being implemented in an escalating manner to classify and/or predict the onset of some neurodegenerative diseases; including Alzheimer's Disease (AD); this could be attributed to the fact of the abundance of data and powerful computers. The objective of this work was to deliver a robust classification system for AD and Mild Cognitive Impairment (MCI) against healthy controls (HC) in a low-cost network in terms of shallow architecture and processing. In this study, the dataset included was downloaded from the Alzheimer's disease neuroimaging initiative (ADNI). The classification methodology implemented was the convolutional neural network (CNN), where the diffusion maps, and gray-matter (GM) volumes were the input images. The number of scans included was 185, 106, and 115 for HC, MCI and AD respectively. Tenfold cross-validation scheme was adopted and the stacked mean diffusivity (MD) and GM volume produced an AUC of 0.94 and 0.84, an accuracy of 93.5% and 79.6%, a sensitivity of 92.5% and 62.7%, and a specificity of 93.9% and 89% for AD/HC and MCI/HC classification respectively. This work elucidates the impact of incorporating data from different imaging modalities; i.e. structural Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI), where deep learning was employed for the aim of classification. To the best of our knowledge, this is the first study assessing the impact of having more than one scan per subject and propose the proper maneuver to confirm the robustness of the system. The results were competitive among the existing literature, which paves the way for improving medications that could slow down the progress of the AD or prevent it.

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“…Second, we extend the approach by employing deep convolutional neural networks (CNNs). [29][30][31] Originally, CNNs were developed for image recognition task, 31,32 but recently, CNNs are also been applied to several medical applications, for example, diagnostic chest X-Rays, 33,34 fracture detection, [35][36][37] mammography, 38 low-dose X-ray tomography, [39][40][41] detection of osteoarthritis, 42,43 diagnosis of retinal diseases, 44 Alzheimer's disease diagnostics, [45][46][47] and MRI segmentation. 48 CNNs are also applied to the risk stratification of hypertension from PPG waveform data.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach

Huttunen

Kärkkäinen

Honkala

et al. 2020

Numer Methods Biomed Eng

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Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks. However, collecting and annotating large datasets can be a very cumbersome and expensive task. We tackle these problems with a virtual database approach where training data is generated using computer simulations of related phenomena. Specifically, we concentrate on the following problem: can cardiovascular indices such as aortic elasticity, diastolic and systolic blood pressures, and blood flow from heart be predicted continuously using wearable photoplethysmographic sensors? We simulate the blood flow using a haemodynamic model consisting of the entire human circulation. Repeated evaluation of the simulator allows us to create a database of “virtual subjects” with size that is only limited by available computational resources. Using this database, we train neural networks to predict the cardiac indices from photoplethysmographic signal waveform. We consider two approaches: neural networks based on predefined input features and deep convolutional neural networks taking waveform directly as the input. The performance of the methods is demonstrated using numerical examples, thus carrying out a preliminary assessment of the approaches. The results show improvements in accuracy compared with the previous methods. The improvements are especially significant with indices related to aortic elasticity and maximum blood flow. The proposed approach would provide new means to measure cardiovascular health continuously, for example, with a simple wrist device.

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Diagnosis of Alzheimer's disease with Sobolev gradient‐based optimization and 3D convolutional neural network

Cited by 94 publications

References 61 publications

Towards the automation of early-stage human embryo development detection

Towards the automation of early-stage human embryo development detection

Alzheimer’s disease diagnosis from diffusion tensor images using convolutional neural networks

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach

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