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Superfast prediction of brain tumours through adaptive incremental pre-training and re-training through transfer learning using state-of-the-art ResNet18 architectural model.

Overview

Authors: Sivani Pinnaboina1, Venkata Sowmya Kambhampati1, Kodanda Rama Sastry Jammalamadaka2, Sasi Bhanu Jammalamadaka3
  1. Electronics and Communication Engineering, KLEF University, Green Fields, Vaddeswaram, 522502 Andhra Pradesh India
  2. Computer Science and Engineering, KLEF University, Green Fields, Vaddeswaram, 522502 Andhra Pradesh India
  3. Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology,Bachupalli, Hyderabad, 500118 Telangana India
Institutions: Koneru Lakshmaiah Education Foundation (India); Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology,Bachupalli, Hyderabad, 500118 Telangana India
Journal: Scientific reports, volume 16, issue 1, article 26493
Dates: received 6 March 2026; accepted 29 June 2026; published online 24 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-60525-7 · PMID 42637857 · PMCID PMC13503758 · OpenAlex W7204096350
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Machine learning
Keywords: Brain tumour MRI classification, Adaptive incremental pretraining, Recursive transfer learning, ResNet18, Ultra-fast retraining, Clinical diagnostic intelligence, Cancer, Computational biology and bioinformatics, Engineering, Mathematics and computing, Medical research, Oncology
MeSH: Brain Neoplasms*, Magnetic Resonance Imaging*, Convolutional Neural Networks, Humans (* major topic)
Topic: Brain Tumor Detection and Classification (Neurology, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 76 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Code availability statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41598-026-60525-7.

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Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-60525-7.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 12 keywords, 4 MeSH terms, 30 references.

Cite

This paper

Pinnaboina, S., Kambhampati, V. S., Jammalamadaka, K. R. S., & Jammalamadaka, S. B. (2026). Superfast prediction of brain tumours through adaptive incremental pre-training and re-training through transfer learning using state-of-the-art ResNet18 architectural model. Scientific reports, 16(1), 26493. https://doi.org/10.1038/s41598-026-60525-7

BibTeX

@article{pinnaboina2026superfast,
author = {Pinnaboina, Sivani and Kambhampati, Venkata Sowmya and Jammalamadaka, Kodanda Rama Sastry and Jammalamadaka, Sasi Bhanu},
title = {{Superfast prediction of brain tumours through adaptive incremental pre-training and re-training through transfer learning using state-of-the-art ResNet18 architectural model}},
journal = {Scientific reports},
year = {2026},
month = aug,
volume = {16},
number = {1},
pages = {26493},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-60525-7},
url = {https://doi.org/10.1038/s41598-026-60525-7},
pmid = {42637857},
pmcid = {PMC13503758}
}

RIS

TY - JOUR
AU - Pinnaboina, Sivani
AU - Kambhampati, Venkata Sowmya
AU - Jammalamadaka, Kodanda Rama Sastry
AU - Jammalamadaka, Sasi Bhanu
TI - Superfast prediction of brain tumours through adaptive incremental pre-training and re-training through transfer learning using state-of-the-art ResNet18 architectural model
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/08/24
VL - 16
IS - 1
SP - 26493
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-60525-7
UR - https://doi.org/10.1038/s41598-026-60525-7
LA - en
ER -

CSL-JSON

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