Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU

Zhang, Yudong; Pan, Chen; Sun, Jianfang; Tang, Chaosheng

doi:10.1016/j.jocs.2018.07.003

Cited by 169 publications

(81 citation statements)

References 38 publications

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“…The values of the mask were then determined to minimize the loss through iterative learning. In this study, as an activation function, the rectified linear unit (ReLU) function [43][44][45][46] rather than the Sigmoid function is used as shown in Figure 7. That is because a vanishing gradient (in which a gradient converges to zero) occurs if the Sigmoid function is used [47][48][49].…”

Section: Detection Of Target Regionmentioning

confidence: 99%

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects' regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

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Section: Detection Of Target Regionmentioning

confidence: 99%

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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“…Andere bereits bearbeitete Fragestellungen für Radiomics-Arbeiten waren die Differenzierung von MS und Erkrankungen aus dem Neuromyelitis-Optica-Spektrum [28][29][30] und die Abgrenzung von MS-Patienten von gesunden Kontrollprobanden. Zum letztgenannten Thema existieren auch auf Deep Learning beruhende Studien [31][32][33]. Eitel et al [34] untersuchten hierbei auch, welche Merkmale der Algorithmus zur Klassifikation heranzieht, und konnten so zeigen, dass neben den typischen Läsionen in geringerem Maß auch normal erscheinende Areale, wie z.…”

Section: Integration Klinischer Datenunclassified

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

Eichinger

Zimmer

Wiestler

2020

Rofo

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Background MR imaging is an essential component in managing patients with Multiple sclerosis (MS). This holds true for the initial diagnosis as well as for assessing the clinical course of MS. In recent years, a growing number of computer tools were developed to analyze imaging data in MS. This review gives an overview of the most important applications with special emphasis on artificial intelligence (AI). Methods Relevant studies were identified through a literature search in recognized databases, and through parsing the references in studies found this way. Literature published as of November 2019 was included with a special focus on recent studies from 2018 and 2019. Results There are a number of studies which focus on optimizing lesion visualization and lesion segmentation. Some of these studies accomplished these tasks with high accuracy, enabling a reproducible quantitative analysis of lesion loads. Some studies took a radiomics approach and aimed at predicting clinical endpoints such as the conversion from a clinically isolated syndrome to definite MS. Moreover, recent studies investigated synthetic imaging, i. e. imaging data that is not measured during an MR scan but generated by a computer algorithm to optimize the contrast between MS lesions and brain parenchyma. Conclusion Computer-based image analysis and AI are hot topics in imaging MS. Some applications are ready for use in clinical routine. A major challenge for the future is to improve prediction of expected disease courses and thereby helping to find optimal treatment decisions on an individual level. With technical improvements, more questions arise about the integration of new tools into the radiological workflow. Key Points: Citation Format

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“…By combining both parametric rectified linear unit (PReLU) and dropout techniques. Zhang et al [65] proposed an improved 10-layer convolutional neural network including 7 convolution layer and 3 fully connected layers. They collected 681 healthy control brain slices and 676 multiple sclerosis brain slices to complete their experiment.…”

Section: Related Workmentioning

confidence: 99%

3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images

Bulbul¹,

Islam

Ali

2019

Multimed Tools Appl

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In this paper, we present an approach for identification of actions within depth action videos. First, we process the video to get motion history images (MHIs) and static history images (SHIs) corresponding to an action video based on the use of 3D Motion Trail Model (3DMTM). We then characterize the action video by extracting the Gradient Local Auto-Correlations (GLAC) features from the SHIs and the MHIs. The two sets of features i.e., GLAC features from MHIs and GLAC features from SHIs are concatenated to obtain a representation vector for action. Finally, we perform the classification on all the action samples by using the l2-regularized Collaborative Representation Classifier (l2-CRC) to recognize different human actions in an effective way. We perform evaluation of the proposed method on three action datasets, MSR-Action3D, DHA and UTD-MHAD. Through experimental results, we observe that the proposed method performs superior to other approaches.

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Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU

Cited by 169 publications

References 38 publications

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

AI in Radiology: Where are we today in Multiple Sclerosis Imaging?

3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images

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