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Abstract

Images can store large amounts of data and are useful for transmitting large amounts of information across different geographical locations using different cloud services. This data sharing increases the chances of cyber-attacks on digital images. Blockchain has properties that enable it to work as a solution to this problem, providing enhanced security and unchangeable storage. However, image size poses a challenge in image storage, as it increases the related storage cost. Compressing images using fractional discrete cosine transform (fctDCT) reduces the amount of data required to express an image securely. This paper presents a novel framework for securely storing and retrieving medical images by extracting feature maps from medical images using fctDCT, followed by encoding and storing a feature map on a decentralized cloud and linking it on a blockchain. The integration has been implemented using α angles, which are stored on the blockchain and need to be identical at the storage and retrieval stage, as only the authentic user would have access to unique α angles and the number of coefficients that have been used in storing their medical images. The proposed novel approach offers numerous benefits, including improved data sharing and collaboration, enhanced security, compression, and efficient retrieval and processing of medical image data. The performance of the proposed framework was evaluated in terms of image quality metrics such as mean square error, peak signal-to-noise ratio, structural similarity index measure (SSIM), and multi SSIM by employing it with correct and incorrect α values.

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