Blockchain enabled collective and combined deep learning framework for COVID19 diagnosis

Abstract

The rapid spread of SARS-CoV-2 has highlighted the need for intelligent methodologies in COVID-19 diagnosis. Clinicians face significant challenges due to the virus's fast transmission rate and the lack of reliable diagnostic tools. Although artificial intelligence (AI) has improved image processing, conventional approaches still rely on centralized data storage and training. This reliance increases complexity and raises privacy concerns, which hinder global data exchange. Therefore, it is essential to develop collaborative models that balance accuracy with privacy protection. This research presents a novel framework that combines blockchain technology with a combined learning paradigm to ensure secure data distribution and reduced complexity. The proposed Combined Learning Collective Deep Learning Blockchain Model (CLCD-Block) aggregates data from multiple institutions and leverages a hybrid capsule learning network for accurate predictions. Extensive testing with lung CT images demonstrates that the model outperforms existing models, achieving an accuracy exceeding 97%. Specifically, on four benchmark datasets, CLCD-Block achieved up to 98.79% Precision, 98.84% Recall, 98.79% Specificity, 98.81% F1-Score, and 98.71% Accuracy, showcasing its superior diagnostic capability. Designed for COVID-19 diagnosis, the CLCD-Block framework is adaptable to other applications, integrating AI, decentralized training, privacy protection, and secure blockchain collaboration. It addresses challenges in diagnosing chronic diseases, facilitates cross-institutional research and monitors infectious outbreaks. Future work will focus on enhancing scalability, optimizing real-time performance and adapting the model for broader healthcare datasets.

Keywords: Blockchain technology; Combined learning paradigm; Diagnostic techniques; Ensemble learning; Hybrid capsule learning network; Predictive modeling.

Fig. 1

Framework of the proposed CLCD-block for effective prediction and diagnosis of COVID-19 using blockchain-enabled combined learning.

Fig. 2

Flowchart of the proposed CLCD-block for enhanced prediction and diagnosis of COVID-19 using blockchain-enabled combined learning approach.

Fig. 3

Sample lung CT scan images from dataset 1.

Fig. 4

Sample lung CT scan images from dataset 4.

Fig. 5

Capsule networks combined with ensemble ELM layers are utilized to enhance the accuracy of feature extraction and classification.

Fig. 6

The suggested framework’s combined learning models were strengthened using blockchain technology.

Fig. 7

Layered architecture of blockchain.

Fig. 8

Error distribution plot demonstrating the accuracy of model performance.

Fig. 9

LVC of the proposed model for Datasets 1, 2, 3 and 4.

Fig. 10

Multi-class confusion matrix of the proposed CLCD-block model for dataset 4.

Fig. 11

Performance evaluation of the proposed framework using various metrics across Datasets 1, 2, 3 and 4.

Fig. 12

Performance evaluation of the proposed framework using Specificity metric across Datasets 1, 2, 3 and 4.

Fig. 13

Comparison of the proposed CLCD-Block framework with baseline methods for COVID-19 detection across all the four Datasets.