Hybrid Intelligence-Driven Medical Image Recognition for Remote Patient Diagnosis in Internet of Medical Things

Guo, Zhiwei; Shen, Yu; Wan, Shaohua; Shang, Wen-Long; Yu, Keping

doi:10.1109/jbhi.2021.3139541

Cited by 70 publications

(26 citation statements)

References 30 publications

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“…Artificial intelligence techniques based computer added diagnosis system is significantly necessary for accurate Guo et al. ( 2021 ) and time classification of brain tumor and detection of Brain cancer in IoT healthcare environment. AI based CAD system cloud be easily incorporated in IoT heath care system for diagnosis and treatment of Brain cancer patients.…”

Section: Literature Reviewmentioning

confidence: 99%

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

Haq

Kumar

et al. 2022

J Ambient Intell Human Comput

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The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.

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Section: Literature Reviewmentioning

confidence: 99%

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

Haq

Kumar

et al. 2022

J Ambient Intell Human Comput

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“…Drug target prioritization is an essential step for repositioning as it aims to highlight the potential drug targets for a particular disease. Applying computational methods to analyze and process multi-omics data is an effective approach for achieving this ( Ashburn and Thor, 2004 ; Glicksberg et al, 2014 ; Shameer et al, 2018a ; Pushpakom et al, 2019 ; Guo et al, 2021 ; Rapicavoli et al, 2022 ). Whilst there is now a vast wealth of biochemical and biomedical data in the current era of high-throughput omics technology, our ability to integrate and interpret these data has lagged behind and is presenting a great challenge in disease biology ( Shameer et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…In such a scenario, we recommend the application of a hybrid intelligence platform that enables visual intelligence, quick search, contextual interpretations with quantitative approaches as a way to address this problem. Hybrid intelligence systems have been developed to address challenging problems in biomedicine, including remote patient diagnosis ( Abu-Doleh et al, 2012 ; Li et al, 2014a ; Akata et al, 2020 ; Guo et al, 2021 ; Weissler et al, 2021 ). However, such approaches are not readily available to address challenges in data integration and mining associated with drug target prioritization and drug repositioning.…”

Section: Introductionmentioning

confidence: 99%

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

et al. 2022

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Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

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“…In recent years, with the healthy development of the regional construction industry and the steady improvement of the level of safety management, the number of construction safety accidents has been significantly reduced, but there is still a significant gap compared with the western developed countries [ 4 – 6 ]. After research, this paper decided to use the combination of expert scoring and AHP to obtain more objective, accurate, and real sample data, which is very important for the follow-up research work [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Neural Networks-Based Safety Evaluation for Smart Construction Devices

Wang

Zhou

2022

Computational Intelligence and Neuroscience

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Based on the theory of lightweight neural networks, this paper presents a safety evaluation model for smart construction devices. The model index system includes the internal logical relationship between the input and output indexes, and the input indexes are specifically refined. According to the safety evaluation results, the article observes what type of accidents will occur at the construction site. According to the detailed and specific output index system, the six input factor layer indicators correspond to the indicators of several multiple network index layers, respectively. In the simulation process, MATLAB software was used to write the multiple neural network model program for the safety evaluation of the construction site, and the appropriate multiple network structure and related parameters were selected. The experimental results show that the multiple neural networks are trained by collecting 10 expert evaluation samples, and the trained multiple neural networks are applied to real construction cases. Comparing the two sets of data, it can be seen that the gap is relatively small, and the sample training is better. The multiple neural networks have relatively good evaluation performance. The method has a fast calculation speed and effectively improves the efficiency and practical value of safety evaluation.

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Hybrid Intelligence-Driven Medical Image Recognition for Remote Patient Diagnosis in Internet of Medical Things

Cited by 70 publications

References 30 publications

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

Lightweight Neural Networks-Based Safety Evaluation for Smart Construction Devices

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