Year: 2025
Type: article
Abstract: : Classification of medical images is crucial in healthcare, especially for disease diagnosis and treatment planning. However, privacy concerns regarding patient information hinder the use of centralized machine learning models. Federated learning (fl) provides a promising solution by enabling collaborative model training across distributed data while ensuring information confidentiality. This study evaluates the use of Models trained beforehand in fl for categorization of healthcare images, specifically using ct scan images. The local models, based on resnet, contribute to the construction of a global model. Pre-trained models, trained on extensive datasets, improve the operational efficiency and classification effectiveness of fl models without compromising data confidentiality. Keywords: categorization of healthcare images, Federated learning, Models trained beforehand, EfficientNet, Resnet ,CNN2D, COVID-19, sophisticated neural networks
DOI: 10.55041/ijsrem47001
Author: M .Brahmaiah
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410127066