Abstract
The Internet of Things (IoT) has made significant progress in the dissemination of healthcare data and corresponding mandatory actions in today’s world, where internet methods operate to automate and computerize numerous commercial as well as domestic applications. Data integrity and healthcare data distribution among privacy intermediate nodes are two major issues in the current scenario. Data must be encrypted to safeguard the confidentiality of sensitive data transferred between nodes, particularly healthcare-related data records. With the help of a trust modeling m-health application, these studies propose a novel method for medical image classification using cyber block chain Ccoud module-based image encryption. The medical image has been collected here, processed to remove noise, and the image has been resized. Using a fast convolution neural network (Fa_ConVolNet), this image has been classified. Then, the IoT cyber blockchain module-based Lorenz-Chaotic Encryption was used to encrypt this classified image. Machine learning and security analysis are used in the classification phase of parametric analysis. Accuracy, precision, recall, f-1 score, data security rate, PSNR, MSE, encryption time, and decryption time are the parameters analysed. It is also longer-lasting and more effective. Classification results obtained by proposed methodisexactness of 98%, correctness of 93%, recall of 92%, f-measure of 94% and network security analysis in terms of data security rate of 93%, PSNR of 77%, MSE of 78%, encryption time of 63 ms, decryption timeof 61 ms as.
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Funding
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Kingdom of Saudi Arabia for funding this work through Large Groups RGP.2/119/43.
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Qamar, S. Machine learning in cloud-based trust modeling in M-health application using classification with image encryption. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08145-5
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DOI: https://doi.org/10.1007/s00500-023-08145-5