Today, the internet of things (IoT) is becoming more common and finds applications in several domains, especially in the healthcare sector. Due to the rising demands of IoT, a massive quantity of sensing data gets generated from diverse sensing devices. Artificial intelligence (AI) techniques are vital for providing a scalable and precise analysis of data in real time. But the design and development of a useful big data analysis technique face a few challenges, like centralized architecture, security, and privacy, resource constraints, and the lack of adequate training data. On the other hand, the rising blockchain technology offers a decentralized architecture. It enables secure sharing of data and resources to the different nodes of the IoT network and is promoted for removing centralized control and resolving the problems of AI. This study develops an optimal deep-learning-based secure blockchain (ODLSB) enabled intelligent IoT and healthcare diagnosis model. The proposed model involves three major processes: secure transaction, hash value encryption, and medical diagnosis. The ODLSB technique comprises the orthogonal particle swarm optimization (OPSO) algorithm for the secret sharing of medical images. In addition, the hash value encryption process takes place using neighborhood indexing sequence (NIS) algorithm. At last, the optimal deep neural network (ODNN) is applied as a classification model to diagnose the diseases. The utilization of OPSO algorithm for secret sharing and optimal parameter tuning process shows the novelty of the work. We carried out detailed experiments to validate the outcome of the proposed method, and several aspects of the results are considered. At the time of the diagnosis process, the OPSO-DNN model has yielded superior results, with the highest sensitivity (92.75%), specificity (91.42%), and accuracy (93.68%).

如今，物联网（IoT）变得越来越普遍，并在多个领域找到了应用，特别是在医疗保健领域。由于物联网的需求不断增长，各种传感设备产生了大量的传感数据。人工智能（AI）技术对于实时提供可扩展且精确的数据分析至关重要。但是，一种有用的大数据分析技术的设计和开发面临一些挑战，例如集中式体系结构，安全性和隐私性，资源限制以及缺乏足够的培训数据。另一方面，新兴的区块链技术提供了一种去中心化的架构。它可以安全地将数据和资源共享到IoT网络的不同节点，并被推广用于消除集中控制并解决AI问题。这项研究开发了一种最佳的基于深度学习的安全区块链（ODLSB）支持的智能物联网和医疗诊断模型。提出的模型涉及三个主要过程：安全交易，哈希值加密和医疗诊断。ODLSB技术包括用于医学图像秘密共享的正交粒子群优化（OPSO）算法。此外，使用邻居索引序列（NIS）算法进行哈希值加密过程。最后，将最优深度神经网络（ODNN）作为诊断疾病的分类模型。利用OPSO算法进行秘密共享和优化参数调整过程表明了这项工作的新颖性。我们进行了详细的实验，以验证所提出方法的结果，并考虑了结果的几个方面。在诊断过程中，OPSO-DNN模型产生了优异的结果，具有最高的灵敏度（92.75％），特异性（91.42％）和准确性（93.68％）。