---
title: "Graph transformer GANs with Osprey optimization for breast cancer detection in histopathology"
id: "pubmed-42717119"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42717119"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42717119/"
doi: "10.1007/s10735-026-10952-7"
published_at: "2026-09-10T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Graph transformer GANs with Osprey optimization for breast cancer detection in histopathology
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42717119
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42717119/)
- **DOI:** [10.1007/s10735-026-10952-7](https://doi.org/10.1007%2Fs10735-026-10952-7)
- **Published At:** 2026-09-10T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- This study proposes GTGAN-GMM-OOA, a graph-based deep learning framework for histopathology classification of **breast cancer** that explicitly models spatial relationships among tissue regions rather than relying on independent image features. - Key components include graph transformer learning to capture structural dependencies, graph-masked modeling to strengthen representation learning, and the **Osprey Optimization Algorithm (OOA)** to tune model configuration adaptively. - Image-level enhancements used are ISGIF (Iterative Self-Guided Image Filter) pre-processing and a scale-aware SMT-U-Net++ architecture to learn discriminative tissue features across magnifications. - The authors evaluated the approach on the publicly available **BreakHis** histopathology database, reporting per-magnification accuracies of 99.9% (40X), 99.7% (100X), 99.9% (200X), and 99.5% (400X). - Five-fold cross-validation produced an average accuracy of 99.75% with a standard deviation of ±0.12%, which the authors present as evidence of stability and effectiveness for graph-based BC histopathology classification. - The paper situates the method relative to conventional image-based approaches and emphasizes modeling of tissue-region dependencies via graph learning and masked graph representations. - Declared conflicts: authors report no conflicts of interest. The article contains no human or animal experimental procedures by the authors and all authors consented to publication. - Keywords and MeSH terms include Breast Neoplasms, Graph Neural Networks, Algorithms, Image Processing, Computer-Assisted methods, and diagnostic pathology-related indexing terms.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliations * 1 Department of Computer Science and Engineering, Mangalam College of Engineering, Kottayam, Kerala, India. sabugeorge24@gmail.com. * 2 Department of Computer Sciences & Engineering, Sharda School of Computing Science & Engineering, Sharda University, Greater Noida, G B Nagar, Uttar Pradesh, India. * 3 Department of Computer Science and Engineering, KL University, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522502, India. * 4 Department of Electronics and Communication Engineering, Sri Shanmugha College of Engineering and Technology, Sangagiri, Salem, Tamil Nadu, 637304, India. * 5 Director - IQAC at Sri Shanmugha Educational Institutions, Sankari, Salem, Tamil Nadu, 637304, India. * PMID: **42717119** * DOI: [ 10.1007/s10735-026-10952-7 ](https://doi.org/10.1007/s10735-026-10952-7) Item in Clipboard # Enhanced breast cancer detection in histopathology images using graph transformer GANs with graph masked modeling and osprey optimization Sabu George et al. J Mol Histol. 2026. Show details Display options Display options Format Abstract PubMed PMID J Mol Histol Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22J+Mol+Histol%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22J+Mol+Histol%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42717119/) . 2026 Sep 10;57(5):302. doi: 10.1007/s10735-026-10952-7. ### Authors [Sabu George](https://pubmed.ncbi.nlm.nih.gov/?term=George+S&cauthor_id=42717119)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42717119/#short-view-affiliation-1 "Department of Computer Science and Engineering, Mangalam College of Engineering, Kottayam, Kerala, India. sabugeorge24@gmail.com."), [Rajiv Kumar Nath](https://pubmed.ncbi.nlm.nih.gov/?term=Nath+RK&cauthor_id=42717119)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42717119/#short-view-affiliation-2 "Department of Computer Sciences & Engineering, Sharda School of Computing Science & Engineering, Sharda University, Greater Noida, G B Nagar, Uttar Pradesh, India."), [S K Sanjeera](https://pubmed.ncbi.nlm.nih.gov/?term=Sanjeera+SK&cauthor_id=42717119)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42717119/#short-view-affiliation-3 "Department of Computer Science and Engineering, KL University, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522502, India."), [K Paul Joshua](https://pubmed.ncbi.nlm.nih.gov/?term=Joshua+KP&cauthor_id=42717119)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42717119/#short-view-affiliation-4 "Department of Electronics and Communication Engineering, Sri Shanmugha College of Engineering and Technology, Sangagiri, Salem, Tamil Nadu, 637304, India.")[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42717119/#short-view-affiliation-5 "Director - IQAC at Sri Shanmugha Educational Institutions, Sankari, Salem, Tamil Nadu, 637304, India.") ### Affiliations * 1 Department of Computer Science and Engineering, Mangalam College of Engineering, Kottayam, Kerala, India. sabugeorge24@gmail.com. * 2 Department of Computer Sciences & Engineering, Sharda School of Computing Science & Engineering, Sharda University, Greater Noida, G B Nagar, Uttar Pradesh, India. * 3 Department of Computer Science and Engineering, KL University, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522502, India. * 4 Department of Electronics and Communication Engineering, Sri Shanmugha College of Engineering and Technology, Sangagiri, Salem, Tamil Nadu, 637304, India. * 5 Director - IQAC at Sri Shanmugha Educational Institutions, Sankari, Salem, Tamil Nadu, 637304, India. * PMID: **42717119** * DOI: [ 10.1007/s10735-026-10952-7 ](https://doi.org/10.1007/s10735-026-10952-7) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract Breast cancer (BC) diagnosis from histopathology images requires accurate identification of malignant tissue while accounting for complex tissue structures and variations in image magnification. In order to overcome these problems, this study suggests GTGAN-GMM-OOA, a graph-based framework that jointly models spatial relationships among tissue regions, learns masked graph representations, and adaptively optimizes model parameters. Unlike conventional image-based approaches that primarily focus on independent visual features, the proposed framework explicitly captures structural dependencies between tissue regions through graph transformer learning. It strengthens representation learning through graph-masked modeling. The Osprey Optimization Algorithm (OOA) is further employed to optimize the model configuration. ISGIF pre-processing and SMT-U-Net++ are introduced to improve image quality and learn discriminative tissue features. Using the BreakHis database, the proposed technique is capable of obtaining 99.9%, 99.7%, 99.9%, and 99.5% of accuracies for 40X, 100X, 200X, and 400X magnification levels, respectively. Additionally, five-fold cross validation is used to validate the stability of the developed technique, with an average accuracy of 99.75% and standard deviation of ±0.12%. The obtained results prove the efficiency and effectiveness of the developed technique for graph-based BC histopathology classification. **Keywords:** Breast cancer; Graph transformer GANs with graph masked modeling; Histopathology images; Iterative self-guided image filter; Osprey optimization algorithm; Scale-aware modulation meet transformer with U-Net++. © 2026. The Author(s), under exclusive licence to Springer Nature B.V. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declarations. Conflict of interest: Authors declare that they have no conflict of interest. Ethics approval: This article does not contain any studies with human participants or animals performed by any of the authors. Consent to participate: All the authors involved have agreed to participate in this submitted article. Consent for publication: All the authors involved in this manuscript give full consent for publication of this submitted article. ## References 1. 1. Abdulaal AH, Valizadeh M, Amirani MC, Shah AS (2024) A self-learning deep neural network for classification of breast histopathological images. Biomed Signal Process Control 87:105418 - [DOI](https://doi.org/10.1016/j.bspc.2023.105418) 2. 1. Addo D, Zhou S, Sarpong K, Nartey OT, Abdullah MA, Ukwuoma CC, Al-antari MA (2024) A hybrid lightweight breast cancer classification framework using histopathological images. Biocybern Biomed Eng 44(1):31–54 - [DOI](https://doi.org/10.1016/j.bbe.2023.12.003) 3. 1. Agarwal P, Yadav A, Mathur P (2022) Breast cancer prediction on BreaKHis dataset using deep CNN and transfer learning model. Data engineering for smart systems. Springer, Singapore, pp 77–88 - [DOI](https://doi.org/10.1007/978-981-16-2641-8_8) 4. 1. Akbar SB, Thanupillai K, Sundararaj S (2022) Combining the advantages of AlexNet convolutional deep neural network optimized with Anopheles search algorithm based feature extraction and random forest classifier for COVID-19 classification. Concurr Comput Pract Exp 34(15):e6958 - [DOI](https://doi.org/10.1002/cpe.6958) 5. 1. Ali MD, Saleem A, Elahi H, Khan MA, Khan MI, Yaqoob MM, Al-Rasheed A (2023) Breast cancer classification through meta-learning ensemble technique using convolution neural networks. Diagnostics (Basel) 13(13):2242 - [DOI](https://doi.org/10.3390/diagnostics13132242) - [PubMed](https://pubmed.ncbi.nlm.nih.gov/37443636/) - [PMC](https://pmc.ncbi.nlm.nih.gov/articles/10341268/) Show all 44 references ## MeSH terms * Algorithms Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Algorithms%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Algorithms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42717119/) * Breast Neoplasms* / diagnosis Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Breast+Neoplasms%2Fdiagnosis%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Breast+Neoplasms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42717119/) * Breast Neoplasms* / diagnostic imaging Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Breast+Neoplasms%2Fdiagnostic+imaging%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Breast+Neoplasms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42717119/) * Breast Neoplasms* / pathology Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Breast+Neoplasms%2Fpathology%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Breast+Neoplasms) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42717119/) * Female Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Female%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Female) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42717119/) * Graph Neural Networks Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Graph+Neural+Networks%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Graph+Neural+Networks) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42717119/) * Humans Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Humans%22%5BMeSH%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Humans) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42717119/) * Image Processing, Computer-Assisted* / methods Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Image+Processing%2C+Computer-Assisted%2Fmethods%22%5BMAJR%5D&sort=date&sort_order=desc) * [ Search in MeSH ](https://www.ncbi.nlm.nih.gov/mesh?term=Image+Processing%2C+Computer-Assisted) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42717119/) [x] Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM **Send To** * [Clipboard](https://pubmed.ncbi.nlm.nih.gov/42717119/) * [Email](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42717119%2F%23open-email-panel) * [Save](https://pubmed.ncbi.nlm.nih.gov/42717119/) * [My Bibliography](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42717119%2F%23open-bibliography-panel) * [Collections](https://account.ncbi.nlm.nih.gov/?back_url=https%3A%2F%2Fpubmed.ncbi.nlm.nih.gov%2F42717119%2F%23open-collections-panel) * [Citation Manager](https://pubmed.ncbi.nlm.nih.gov/42717119/) [x] NCBI Literature Resources [MeSH](https://www.ncbi.nlm.nih.gov/mesh/) [PMC](https://www.ncbi.nlm.nih.gov/pmc/) [Bookshelf](https://www.ncbi.nlm.nih.gov/books) [Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). 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