{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T19:49:19Z","timestamp":1770061759080,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PubArt program of the National University of Science and Technology POLITEHNICA, Bucharest"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Brain cancer detection is dependent on multiple diagnostic techniques. Histopathological diagnosis, although the most effective, requires the extraction of cancer cells, which is very risky and painful for a patient. Another popular noninvasive image-based diagnosis technique is magnetic resonance imaging (MRI). Brain diagnosis data based on MRI scans are highly sensitive and private. This study proposes a single-to-multimodal transformation technique that generates synthetic histopathological data from expert-labelled brain MRI datasets using transfer learning techniques. Furthermore, to preserve a patient\u2019s privacy, an encryption module is used to encrypt the MRI image data and the respective histopathological notations. The Kruskal\u2013Wallis statistical test is also used to analyze the radiogemomics dataset. The trained module is also encrypted, only to be accessed by authorized medical personnel. The transfer learning modules (CNN-based deep learning model, ViT, Resnet101, and YOLOv8) are used here and achieved 99.60% accuracy.<\/jats:p>","DOI":"10.3390\/a19020112","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T09:00:33Z","timestamp":1770022833000},"page":"112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Privacy-Preserving Synthetic Histopathological Single-to-Multimodal Data Generation from Brain MRI Using Transfer Learning"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1785-8586","authenticated-orcid":false,"given":"Mahendra Kumar","family":"Gourisaria","sequence":"first","affiliation":[{"name":"School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhijit","family":"Roy","sequence":"additional","affiliation":[{"name":"IIIT, Guwahati 781015, Assam, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1056-5617","authenticated-orcid":false,"given":"Amitkumar V.","family":"Jha","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0878-7405","authenticated-orcid":false,"given":"Bhargav","family":"Appasani","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saurabh","family":"Bilgaiyan","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar 751024, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3218-9218","authenticated-orcid":false,"given":"Alin Gheorghita","family":"Mazare","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Communication and Computers, Pite\u0219ti University Centre, The National University of Science and Technology POLITEHNICA Bucharest, 110040 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9311-7598","authenticated-orcid":false,"given":"Nicu","family":"Bizon","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Communication and Computers, Pite\u0219ti University Centre, The National University of Science and Technology POLITEHNICA Bucharest, 110040 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,1]]},"reference":[{"key":"ref_1","first-page":"351","article-title":"MicroRNA-494 promotes the proliferation and migration of human glioma cancer cells through the protein kinase B\/mechanistic target of rapamycin pathway by phosphatase and tensin homolog expression","volume":"41","author":"Han","year":"2019","journal-title":"Oncol. 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