The Roles of Cloud-Based Systems on the Cancer-Related Studies: A Systematic Literature Review

Xu, Binghui; Zhou, Fengcheng

doi:10.1109/access.2022.3181147

Cited by 5 publications

(2 citation statements)

References 174 publications

(145 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in recent years, the presence of spatial heterogeneity in data has become increasingly common in various fields such as survey studies, surveillance efforts, and longitudinal studies, particularly in cancer-related research [14]. For instance, the Surveillance, Epidemiology, and End Results (SEER) Program, the China Health and Retirement Longitudinal Study (CHARLS), and the China Northwest Cohort (CNC) often involve the collection of data at small geographical levels (such as communities or counties), which are subsequently aggregated at larger levels.…”

Section: Introductionmentioning

confidence: 99%

Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach

Cai,

Hu,

Yang

et al. 2024

BMC Med Res Methodol

View full text Add to dashboard Cite

Background Outliers, data points that significantly deviate from the norm, can have a substantial impact on statistical inference and provide valuable insights in data analysis. Multiple methods have been developed for outlier detection, however, almost all available approaches fail to consider the spatial dependence and heterogeneity in spatial data. Spatial data has diverse formats and semantics, requiring specialized outlier detection methodology to handle these unique properties. For now, there is limited research exists on robust spatial outlier detection methods designed specifically under the spatial error model (SEM) structure. Method We propose the Spatial-Θ-Iterative Procedure for Outlier Detection (Spatial-Θ-IPOD), which utilizes a mean-shift vector to identify outliers within the SEM. Our method enables an effective detection of spatial outliers while also providing robust coefficient estimates. To assess the performance of our approach, we conducted extensive simulations and applied it to a real-world empirical study using life expectancy data from multiple countries. Results Simulation results showed that the masking and JD (Joint Detection) indicators of our Spatial-Θ-IPOD method outperformed several commonly used methods, even in high-dimensional scenarios, demonstrating stable performance. Conversely, the Θ-IPOD method proved to be ineffective in detecting outliers when spatial correlation was present. Moreover, our model successfully provided reliable coefficient estimation alongside outlier detection. The proposed method consistently outperformed other models (both robust and non-robust) in most cases. In the empirical study, our proposed model successfully detected outliers and provided valuable insights in the modeling process. Conclusions Our proposed Spatial-Θ-IPOD offers an effective solution for detecting spatial outliers for SEM while providing robust coefficient estimates. Notably, our approach showcases its relative superiority even in the presence of high leverage points. By successfully identifying outliers, our method enhances the overall understanding of the data and provides valuable insights for further analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach

Cai,

Hu,

Yang

et al. 2024

BMC Med Res Methodol

View full text Add to dashboard Cite

show abstract

“…One of the most important benefits of cloud computing in healthcare is enhanced interoperability and data accessibility. Healthcare generates abundance of data, including medical images, electronic health records (EHRs), research data, and patient histories [2]. Storing and managing this data on local servers can be inefficient, costly, and prone to data silos.…”

Section: Introductionmentioning

confidence: 99%

Reptile Search Algorithm with Deep Convolutional Neural Network for Cloud Assisted Colorectal Cancer Detection and Classification

S. Murugappan

2023

tjjpt

View full text Add to dashboard Cite

Cloud-based automatic colorectal cancer (CC) detection involves the usage of cloud computing technology and system to help in the earlier and accurate diagnosis of CC in medical images and patient information. This cloud-based technology aims to improve the efficiency and reliability of CC screening, monitoring, and diagnoses. Automatic CC detection refers to the use of computer-based technology and systems to aid in the earlier and accurate detection of CC in patient data and medical images. This automated system aims to increase the reliability and efficiency of CC monitoring, screening, and diagnosis. Deep learning (DL) methods, especially convolutional neural networks (CNNs), exhibit promising results in automatic CC diagnosis. They can be trained on wide-ranging datasets of medical images to learn patterns and features related to precancerous and cancerous lesion. This study develops a new Reptile Search Algorithm with Deep Learning for Colorectal Cancer Detection and Classification (RSADL-CCDC) technique. The main aim of the RSADL-CCDC method focuses on the automaticclassification and recognition of the CC in the cloud environment. Once the medical images are stored in the cloud server, the detection process is carried out. In the presented RSADL-CCDC approach, the initial stage of preprocessing is performed by bilateral filtering (BF) approach. For feature extraction, the RSADL-CCDC technique applies ShuffleNetv2 model. Besides, the recognition and classification of CC take place using convolutional autoencoder (CAE) model. Finally, the hyperparameter tuning of the CAE technique takes place by utilizing RSA. The experimental validation of the RSADL-CCDC system is performed on benchmark medical database. Extensive results stated the enhanced performance of the RSADL-CCDC technique on CC recognition over other models with respect tovarious actions.

show abstract

Smart IoT_Cloud-Based ML Technique to Classify Breast Cancer

Prusty,

Dash,

Prusty

et al. 2023

2023 2nd International Conference on Ambient Intelligence in Health Care (ICAIHC)

View full text Add to dashboard Cite

The Roles of Cloud-Based Systems on the Cancer-Related Studies: A Systematic Literature Review

Cited by 5 publications

References 174 publications

Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach

Outlier detection in spatial error models using modified thresholding-based iterative procedure for outlier detection approach

Reptile Search Algorithm with Deep Convolutional Neural Network for Cloud Assisted Colorectal Cancer Detection and Classification

Smart IoT_Cloud-Based ML Technique to Classify Breast Cancer

Contact Info

Product

Resources

About