Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis

Vasconcelos, Francisco Fábio Ximenes; Sarmento, Róger Moura; Filho, Pedro Pedrosa Rebouças; Albuquerque, Victor Hugo C. de

doi:10.1016/j.engappai.2020.103585

Cited by 35 publications

(18 citation statements)

References 34 publications

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“…In [74], Vasconcelos et al proposed a new method called adaptive brain tissue density analysis (adaptive ABTD) to improve the detection and classification of strokes. Edge computing devices provided low computation and cost and reduced time consumption in detection and diagnosis.…”

Section: Edge-and Cloud-based Smart Health Carementioning

confidence: 99%

A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

2021

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Section: Edge-and Cloud-based Smart Health Carementioning

confidence: 99%

A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

2021

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“…In (9), let be all the samples of an incremental training round and ∅(•) be the output of the CNN feature extractor and be the incremental training round. shuffled after every epoch to maintain the classification accuracy.…”

Section: Incremental Learning On the Cloudmentioning

confidence: 99%

“…While there have been several applications of deep learning in the Internet of Things (IoT) [6][7][8][9][10][11][12][13] but for incremental learning, the majority of the research is being carried out on a centralized computer [14,15], i.e., incremental learning has not been explored in the context of IoT where a model has to be partitioned in an edge-cloud architecture [16]. There is a clear for incremental learning to be integrated with IoT because in the real-world, smart devices that are collecting raw data can be geographically spread and new data belonging to new tasks can also be collected over time.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of IoT Stream Sampling and Model Update on the IoT Edge Device for Class Incremental Learning in an Edge-Cloud System

2021

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With the exponential rise of the number of IoT devices, the amount of data being produced is massive. Thus, it is unfeasible to send all the raw data directly to the cloud for processing, especially for data that is high dimensional. Training deep learning models incrementally evolves the model over time and eliminates the need to statically training the models with all the data. However, the integration of class incremental learning and the Internet of Things (IoT) is a new concept and is not yet mature. In the context of IoT and deep learning, the transmission cost of data in the edge-cloud architecture is a challenge. We demonstrate a novel sample selection method that discards certain training images on the IoT edge device that reduces transmission cost and still maintains class incremental learning performance. It can be unfeasible to transmit all parameters of a trained model back to the IoT edge device. Therefore, we propose an algorithm to find only the useful parameters of a trained model in an efficient way to reduce the transmission cost from the cloud to the edge devices. Results show that our proposed methods can effectively perform class-incremental learning in an edge-cloud setting.

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“…De Albuquerque et al (2017) investigate the applications of brain computer interface systems. Some IoT frameworks are proposed to analyze the brain signals, such as brain CT images (Jaiswal et al, 2019;Sarmento et al, 2020;Vasconcelos et al, 2020), MRI (Mallick et al, 2019;Arunkumar et al, 2020), etc. Many applications benefit human daily life.…”

Section: Related Workmentioning

confidence: 99%

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Zhu

Zeng

Wang

et al. 2020

Front. Hum. Neurosci.

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Large-scale neuroscience literature call for effective methods to mine the knowledge from species perspective to link the brain and neuroscience communities, neurorobotics, computing devices, and AI research communities. Structured knowledge can motivate researchers to better understand the functionality and structure of the brain and link the related resources and components. However, the abstracts of massive scientific works do not explicitly mention the species. Therefore, in addition to dictionary-based methods, we need to mine species using cognitive computing models that are more like the human reading process, and these methods can take advantage of the rich information in the literature. We also enable the model to automatically distinguish whether the mentioned species is the main research subject. Distinguishing the two situations can generate value at different levels of knowledge management. We propose SpecExplorer project which is used to explore the knowledge associations of different species for brain and neuroscience. This project frees humans from the tedious task of classifying neuroscience literature by species. Species classification task belongs to the multi-label classification which is more complex than the single-label classification due to the correlation between labels. To resolve this problem, we present the sequence-to-sequence classification framework to adaptively assign multiple species to the literature. To model the structure information of documents, we propose the hierarchical attentive decoding (HAD) to extract span of interest (SOI) for predicting each species. We create three datasets from PubMed and PMC corpora. We present two versions of annotation criteria (mention-based annotation and semantic-based annotation) for species research. Experiments demonstrate that our approach achieves improvements in the final results. Finally, we perform species-based analysis of brain diseases, brain cognitive functions, and proteins related to the hippocampus and provide potential research directions for certain species.

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Artificial intelligence techniques empowered edge-cloud architecture for brain CT image analysis

Cited by 35 publications

References 34 publications

A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare

A Novel Approach of IoT Stream Sampling and Model Update on the IoT Edge Device for Class Incremental Learning in an Edge-Cloud System

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

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