Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection.

Aya E Fawzy, Mohammed E Almandouh, Mostafa Herajy, Mohamed Eisa

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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Authors and funding

4 authors.

Aya E FawzyInformation Technology Management Department, Faculty of Management Technology and Information Systems, Port Said University, Port Said, Egypt. aya_elsayed@himc.psu.edu.eg.

Mohammed E AlmandouhInformation Technology Management Department, Faculty of Management Technology and Information Systems, Port Said University, Port Said, Egypt.

Mostafa HerajyMathematics and Computer Science Department, Faculty of Science, Port Said University, Port Said, Egypt.

Mohamed EisaInformation Technology Management Department, Faculty of Management Technology and Information Systems, Port Said University, Port Said, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As digital imaging in healthcare grows quickly, dealing with vast medical image data is getting trickier. Content-Based Medical Image Retrieval (CBMIR) systems help with this, but they struggle because of the gap between simple image details and what these images mean in a clinical setting. This paper presents a new approach using deep learning for CBMIR that combines Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Explainable AI (XAI). Using the Breast Ultrasound Image (BUSI) dataset for training, this hybrid model classifies images and finds the relevant results based on predictions. It reaches a classification accuracy of 99.24% and performs well in retrieval tasks.

Indexed as

Breast NeoplasmsDeep LearningImage Processing, Computer-AssistedConvolutional Neural NetworksFemaleHumansNeural Networks, ComputerRecurrent Neural NetworksUltrasonography, MammaryCBMIRCNNHybrid modelRNNXAI

Identifiers

PMID41813733

DOI10.1038/s41598-026-38218-y

PMCPMC12979572

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.