A blockchain-integrated smart healthcare framework utilizing dynamic hunting leadership algorithm with deep learning-based disease detection and classification model

Abstract

The healthcare sector has embraced a digital revolution driven by modern technology. Smart healthcare solutions improve patient care by addressing the challenges of traditional methods using large-scale sensor devices. Blockchain (BC) technology ensures secure, decentralized storage and sharing of medical data, fostering intelligent healthcare ecosystems. Robotics and machine learning (ML) also benefit from shared medical data. This manuscript introduces a blockchain-integrated smart healthcare framework utilizing a dynamic hunting leadership algorithm for deep learning-based disease detection and classification (BSHDHL-DLDDC). It focuses on accurate disease diagnosis using deep learning on medical images. BC technology enables secure, tamper-proof storage and privacy-compliant data sharing. Adaptive bilateral filtering (ABF) reduces noise while preserving key image details. An enhanced CapsNet model captures spatial relationships for improved feature extraction. A bi-directional gated recurrent unit (BiGRU) classifier detects and classifies diseases, with performance refined via a dynamic hunting leadership (DHL) algorithm. Simulations confirm the framework’s effectiveness, demonstrating better results compared to existing methods.