Adversarial Graph Learning and Deep Learning Techniques for improving diagnosis within CT and Ultrasound images

Dan-Sebastian, Bacea; Mitrea, Delia-Alexandrina; Nedevschi, Sergiu; Badea, Radu

doi:10.1109/iccp51029.2020.9266242

Cited by 3 publications

(1 citation statement)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Light CNN framework which is mentioned in [33] has shortened the training time with less classification accuracy whereas the works mentioned in [34] and [35] have greater recognition accuracy with high time complexity. The feature extraction mechanism may generate redundant and irrelevant features in feature space subjected to high computational overhead.…”

Section: Related Workmentioning

confidence: 99%

A Computerized Doughty Predictor Framework for Corona Virus Disease: Combined Deep Learning based Approach

2022

KSII TIIS

View full text Add to dashboard Cite

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID + and COVID -. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.

show abstract

Section: Related Workmentioning

confidence: 99%

A Computerized Doughty Predictor Framework for Corona Virus Disease: Combined Deep Learning based Approach

2022

KSII TIIS

View full text Add to dashboard Cite

show abstract

Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review

Bathula,

Gupta,

Merugu

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

The fusion of blockchain and artificial intelligence (AI) marks a paradigm shift in healthcare, addressing critical challenges in securing electronic health records (EHRs), ensuring data privacy, and facilitating secure data transmission. This study provides a comprehensive analysis of the adoption of blockchain and AI within healthcare, spotlighting their role in fortifying security and transparency leading the trajectory for a promising future in the realm of healthcare. Our study, employing the PRISMA model, scrutinized 402 relevant articles, employing a narrative analysis to explore the fusion of blockchain and AI in healthcare. The review includes the architecture of AI and blockchain, examines AI applications with and without blockchain integration, and elucidates the interdependency between AI and blockchain. The major findings include: (i) it protects data transfer, and digital records, and provides security; (ii) enhances EHR security and COVID-19 data transmission, thereby bolstering healthcare efficiency and reliability through precise assessment metrics; (iii) addresses challenges like data security, privacy, and decentralized computing, forming a robust tripod. The fusion of blockchain and AI revolutionize healthcare by securing EHRs, and enhancing privacy, and security. Private blockchain adoption reflects the sector’s commitment to data security, leading to improved efficiency and accessibility. This convergence promises enhanced disease identification, response, and overall healthcare efficacy, and addresses key sector challenges. Further exploration of advanced AI features integrated with blockchain promises to enhance outcomes, shaping the future of global healthcare delivery with guaranteed data security, privacy, and innovation.

show abstract

A bi-stage feature selection approach for COVID-19 prediction using chest CT images

Sen

Saha²,

Chatterjee³

et al. 2021

Appl Intell

View full text Add to dashboard Cite

The rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play an important role for an accurate diagnostic. In the present work, a bi-modular hybrid model is proposed to detect COVID-19 from the chest CT images. In the first module, we have used a Convolutional Neural Network (CNN) architecture to extract features from the chest CT images. In the second module, we have used a bi-stage feature selection (FS) approach to find out the most relevant features for the prediction of COVID and non-COVID cases from the chest CT images. At the first stage of FS, we have applied a guided FS methodology by employing two filter methods: Mutual Information (MI) and Relief-F, for the initial screening of the features obtained from the CNN model. In the second stage, Dragonfly algorithm (DA) has been used for the further selection of most relevant features. The final feature set has been used for the classification of the COVID-19 and non-COVID chest CT images using the Support Vector Machine (SVM) classifier. The proposed model has been tested on two open-access datasets: SARS-CoV-2 CT images and COVID-CT datasets and the model shows substantial prediction rates of 98.39% and 90.0% on the said datasets respectively. The proposed model has been compared with a few past works for the prediction of COVID-19 cases. The supporting codes are uploaded in the Github link: https://github.com/Soumyajit-Saha/A-Bi-Stage-Feature-Selection-on-Covid-19-Dataset

show abstract

Adversarial Graph Learning and Deep Learning Techniques for improving diagnosis within CT and Ultrasound images

Cited by 3 publications

References 32 publications

A Computerized Doughty Predictor Framework for Corona Virus Disease: Combined Deep Learning based Approach

A Computerized Doughty Predictor Framework for Corona Virus Disease: Combined Deep Learning based Approach

Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review

A bi-stage feature selection approach for COVID-19 prediction using chest CT images

Contact Info

Product

Resources

About