Privacy-Enabling Framework for Cloud-Assisted Digital Healthcare Industry

Ansari, Aman Ahmad; Mishra, Bharavi; Gera, Poonam; Khan, Muhammad Khurram; Chakraborty, Chinmay; Mishra, Dheerendra

doi:10.1109/tii.2022.3170148

Cited by 15 publications

(2 citation statements)

References 28 publications

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“…Neural networks could be trained to be doing the same tasks on data that our brains do, such as finding patterns and categorizing different types of information [13,14]. Ansari et al [15] presented a privacy-enabled architecture for cloud-based e-healthcare systems. Their findings show that the suggested framework outperforms competing frameworks while maintaining security.…”

Section: Significance Of Deep Learningmentioning

confidence: 99%

Retracted: Object detection and recognition using deep learning‐based techniques

et al. 2022

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The above article from IET Communications, published online on 17 October 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Significance Of Deep Learningmentioning

confidence: 99%

Retracted: Object detection and recognition using deep learning‐based techniques

et al. 2022

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“…In further studies with expanded data collections, security and privacy issues should be considered for a more widespread application in the clinical setting [ 56 ]. Also, to increase the utilization of additionally collected data, it is necessary to go through the process of generating structured readings [ 57 ] by finding important keywords that are used repeatedly in the readings of various radiologists [ 57 , 58 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

Park,

Kim,

Cha

et al. 2023

Diagnostics

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Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

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