Security is considered the primary challenge in the healthcare industry in the aspect of the ethical and legal perspective of patient medical data. In existing approaches, the accessibility, reliability, and confidentiality of medical data are needed for more security in the healthcare industry. The main obstacles involved in the communication between the smartphone and monitoring devices allow a data theft attack. Hence, the secured model needs to be designed for providing data security in healthcare applications. By avoiding these challenges, this task provides secured intrusion detection with blockchain-based healthcare data transmission with the help of novel intelligence techniques. Initially, in the data acquisition phase, the gathered data from online sources consist of brain, skin, and retinal medical images. Then, the Weight Optimized Deep Belief Network (WO-DBN) checks the nodes to confirm whether the gathered data are in a malicious state or not at the time of transmission. The medical data are secured with the support of encryption and blockchain technology, where the medical images are enciphered by chaotic-map-aided image encryption through optimal key generation. These encrypted images are uploaded to the blockchain for secure data communication to the cloud server. Here, the authorized person can replace the data using the same optimal key and also it can be done after the decryption in the proposed chaotic map. The decrypted data are given to the final disease diagnosis phase for classifying the images without any information loss with the support of a Residual Network (Resnet101), where the final layer is restored by the “Deep Neural Network (DNN) and Long Short Term Memory (LSTM)” to enhance the classification accuracy and it is named Res-LSTM+DNN. The weight optimization in DBN, optimal key generation, and hyper-parameter tuning in classification are done by the Improved Dingo Optimizer (IDOX). From the overall result validation, the accuracy rate of the recommended approach scores 98.6%.

从患者医疗数据的道德和法律角度来看，安全性被认为是医疗保健行业的主要挑战。在现有方法中，为了提高医疗保健行业的安全性，需要医疗数据的可访问性、可靠性和机密性。智能手机和监控设备之间的通信所涉及的主要障碍导致数据盗窃攻击。因此，需要设计安全模型来提供医疗保健应用程序中的数据安全。通过避免这些挑战，该任务借助新颖的智能技术，通过基于区块链的医疗数据传输提供安全的入侵检测。最初，在数据采集阶段，从在线来源收集的数据包括大脑、皮肤和视网膜医学图像。然后，权重优化深度置信网络（WO-DBN）检查节点，以确认收集的数据在传输时是否处于恶意状态。医疗数据在加密和区块链技术的支持下得到保护，其中医疗图像通过最佳密钥生成通过混沌地图辅助图像加密进行加密。这些加密的图像被上传到区块链，以便与云服务器进行安全的数据通信。这里，授权人可以使用相同的最优密钥替换数据，并且也可以在所提出的混沌映射中解密之后完成。解密的数据被提供给最终的疾病诊断阶段，在残差网络（Resnet101）的支持下对图像进行分类，而不会丢失任何信息，其中最后一层由“深度神经网络（DNN）和长短期记忆”恢复(LSTM)”来提高分类精度，命名为Res-LSTM+DNN。DBN 中的权重优化、最优密钥生成以及分类中的超参数调整均由改进的 Dingo 优化器 (IDOX) 完成。从总体结果验证来看，推荐方法的准确率达到98.6%。