Authors : Prabhat Kumar Shah; Ajeet Kumar Soni; Aryan Parnami; Kushagree Gupta

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/3zmccnkx

DOI : https://doi.org/10.38124/ijisrt/25mar1799

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The boom in smart e-healthcare services pop- ping up in far-flung and spread-out places has sparked some real worries about privacy and speed, especially when it comes to handling touchy patient info. Sharing clinical data across cloud platforms only makes it trickier to keep patient privacy locked down, pushing the need for fresh ideas to get people to trust medical research again. This study tackles those big concerns head-on with a new setup that mixes Deep Learning (DL), Blockchain, and Cloud Computing to whip up a fast and secure system for sorting out pathological images. DL models, with all their fancy complexity, can be a handful in cloud setups where crunching data efficiently often takes a hit [8]. Our framework leans on deep learning tricks to nail down ac- curate classifications of pathological images while keeping sensitive medical data under wraps. Blockchain steps in to build a decentralized, tamper-proof record-keeper, boosting the security and openness of the diagnostic gig. On top of that, cloud computing getstapped to smooth out the heavy lifting of those tricky DL models, sidestepping the usual headaches tied to old-school cloud methods.

Keywords : BreaKHis Database, Pixel Size, Ethereum, Blockchain, IPFS, Neural Compression, Computa- Tional Pathology.

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