Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net

Bhatia, Surbhi; Alam, Shadab; Shuaib, Mohammed; Alhameed, Mohammed; Jeribi, Fathe; Alsuwailem, Razan Ibrahim

doi:10.3389/fpubh.2022.858327

Cited by 23 publications

(18 citation statements)

References 45 publications

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“…The area beneath the Receiver Operating Characteristic (ROC) curve ( 38 ) is measured by AUC ( 39 ). True Positives (TP) and True Negatives (TN) are improved by a model with a higher AUC ( 40 ). Table 3 shows the AUC scores for all 15 labels, including the average, for various scales.…”

Section: Resultsmentioning

confidence: 99%

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Bhatia

Alojail

Sengan

et al. 2022

Front. Public Health

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Handwritten prescriptions and radiological reports: doctors use handwritten prescriptions and radiological reports to give drugs to patients who have illnesses, injuries, or other problems. Clinical text data, like physician prescription visuals and radiology reports, should be labelled with specific information such as disease type, features, and anatomical location for more effective use. The semantic annotation of vast collections of biological and biomedical texts, like scientific papers, medical reports, and general practitioner observations, has lately been examined by doctors and scientists. By identifying and disambiguating references to biomedical concepts in texts, medical semantics annotators could generate such annotations automatically. For Medical Images (MedIMG), we provide a methodology for learning an effective holistic representation (handwritten word pictures as well as radiology reports). Deep Learning (DL) methods have recently gained much interest for their capacity to achieve expert-level accuracy in automated MedIMG analysis. We discovered that tasks requiring significant responsive fields are ideal for downscaled input images that are qualitatively verified by examining functional, responsive areas and class activating maps for training models. This article focuses on the following contributions: (a) Information Extraction from Narrative MedImages, (b) Automatic categorisation on image resolution with an impact on MedIMG, and (c) Hybrid Model to Predictions of Named Entity Recognition utilising RNN + LSTM + GRM that perform admirably in every trainee for every input purpose. At the same time, supplying understandable scale weight implies that such multi-scale structures are also crucial for extracting information from high-resolution MedIMG. A portion of the reports (30%) are manually evaluated by trained physicians, while the rest were automatically categorised using deep supervised training models based on attention mechanisms and supplied with test reports. MetaMapLite proved recall and precision, but also an F1-score equivalent for primary biomedicine text search techniques and medical text examination on many databases of MedIMG. In addition to implementing as well as getting the requirements for MedIMG, the article explores the quality of medical data by using DL techniques for reaching large-scale labelled clinical data and also the significance of their real-time efforts in the biomedical study that have played an instrumental role in its extramural diffusion and global appeal.

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Section: Resultsmentioning

confidence: 99%

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

Bhatia

Alojail

Sengan

et al. 2022

Front. Public Health

Self Cite

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“…Reference [26] analyzed the possible privacy information leakage problem in STIX network security threat intelligence sharing standard, and tried to solve the privacy information leakage problem by using an improved data sharing protocol. The above research work has played a role in privacy protection from a unilateral dimension, but cannot protect the privacy information of the intelligence sharing party, the intelligence user and the intelligence-related parties at the same time [27][28][29][30][31][32].…”

Section: Related Workmentioning

confidence: 99%

A Secure Framework for Blockchain Transactions Protection

Al-Sharu¹,

Qabalin²,

Naser³

et al. 2023

Computer Systems Science and Engineering

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One of the most extensively used technologies for improving the security of IoT devices is blockchain technology. It is a new technology that can be utilized to boost the security. It is a decentralized peer-to-peer network with no central authority. Multiple nodes on the network mine or verify the data recorded on the Blockchain. It is a distributed ledger that may be used to keep track of transactions between several parties. No one can tamper with the data on the blockchain since it is unchangeable. Because the blocks are connected by hashes, the transaction data is safe. It is managed by a system that is based on the consensus of network users rather than a central authority. The immutability and tamper-proof nature of blockchain security is based on asymmetric cryptography and hashing. Furthermore, Blockchain has an immutable and tamper-proof smart contract, which is a logic that enforces the Blockchain's laws. There is a conflict between the privacy protection needs of cyber-security threat intelligent (CTI) sharing and the necessity to establish a comprehensive attack chain during blockchain transactions. This paper presents a blockchain-based data sharing paradigm that protects the privacy of CTI sharing parties while also preventing unlawful sharing and ensuring the benefit of legitimate sharing parties. It builds a full attack chain using encrypted threat intelligence and exploits the blockchain's backtracking capacity to finish the decryption of the threat source in the attack chain. Smart contracts are also used to send automatic early warning replies to possible attack targets. Simulation tests are used to verify the feasibility and efficacy of the suggested model.

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“…A competitive economy has likewise assures that the currencies assigned to equitably [16]. Nevertheless, cryptocurrency mining is energy-intensive since the process of manufacture a new set-top token requires use of big, mighty machines solve complex cryptographic issues [17]. Each transaction must be validated by the mining since Bitcoin is distributed networks that do not have substantial body.…”

Section: Cryptocurrency Miningmentioning

confidence: 99%

A Novel Optimization for GPU Mining Using Overclocking and Undervolting

et al. 2022

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Cryptography and associated technologies have existed for a long time. This field is advancing at a remarkable speed. Since the inception of its initial application, blockchain has come a long way. Bitcoin is a cryptocurrency based on blockchain, also known as distributed ledger technology (DLT). The most well-known cryptocurrency for everyday use is Bitcoin, which debuted in 2008. Its success ushered in a digital revolution, and it currently provides security, decentralization, and a reliable data transport and storage mechanism to various industries and companies. Governments and developing enterprises seeking a competitive edge have expressed interest in Bitcoin and other cryptocurrencies due to the rapid growth of this recent technology. For computer experts and individuals looking for a method to supplement their income, cryptocurrency mining has become a big source of anxiety. Mining is a way of resolving mathematical problems based on the processing capacity and speed of the computers employed to solve them in return for the digital currency incentives. Herein, we have illustrated benefits of utilizing GPUs (graphical processing units) for cryptocurrency mining and compare two methods, namely overclocking and undervolting, which are the superior techniques when it comes to GPU optimization. The techniques we have used in this paper will not only help the miners to gain profits while mining cryptocurrency but also solve a major flaw; in order to mitigate the energy and resources that are consumed by the mining hardware, we have designed the mining hardware to simultaneously run longer and consume much less electricity. We have also compared our techniques with other popular techniques that are already in existence with respect to GPU mining.

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Retinal Vessel Extraction via Assisted Multi-Channel Feature Map and U-Net

Cited by 23 publications

References 45 publications

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

An efficient modular framework for automatic LIONC classification of MedIMG using unified medical language

A Secure Framework for Blockchain Transactions Protection

A Novel Optimization for GPU Mining Using Overclocking and Undervolting

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