Your browser doesn't support javascript.
Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT Network
ACM Transactions on Multimedia Computing, Communications and Applications ; 18(2 S), 2022.
Article in English | Scopus | ID: covidwho-2214024
ABSTRACT
In this paper, a brownfield Internet of Medical Things network is introduced for imaging data that can be easily scaled out depending on the objectives, functional requirements, and the number of facilities and devices connected to it. This is further used to develop a novel Content-based Medical Image Retrieval framework. The developed framework uses DenseNet-201 architecture for generating the image descriptors. Then for classification, the optimized Deep Neural Network model has been configured through a population-based metaheuristic Differential Evolution. Differential Evolution iteratively performs the joint optimization of hyperparameters and architecture of Deep Neural Networks. The competence of the proposed model is validated on three publicly available datasets Brain Tumor MRI dataset, Covid-19 Radiography database, and Breast Cancer MRI dataset, and by comparing it with selected models over different aspects of performance evaluation. Results show that the convergence rate of the proposed framework is very fast, and it achieves at least 97.28% accuracy across all the models. © 2022 Association for Computing Machinery.
Keywords

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: ACM Transactions on Multimedia Computing, Communications and Applications Year: 2022 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: ACM Transactions on Multimedia Computing, Communications and Applications Year: 2022 Document Type: Article