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This study shows a deep learning-based framework for the automatic classification of benign and malignant breast lesions using MRI images. To guarantee accurate patient-level annotations, a large-scale dataset comprising around 25,000 breast MRI images was constructed using an extensive preprocessing and labeling procedure. A hybrid feature-fusion model that integrates MobileNetV2 and VGG16 in a parallel structure was extensively compared and examined with a number of transfer learning-based convolutional neural network designs, such as VGG16, MobileNetV2, and DenseNet121. Each model was trained utilizing a two-stage approach that included frozen training and fine-tuning after being initiated with ImageNet pre-trained weights. Accuracy, precision, recall, specificity, F1-score, and ROC-AUC metrics were used to evaluate the model\u2019s performance. Having 97.79% accuracy, 96.62% recall, and a ROC\u2013AUC value of 0.9983, the hybrid MobileNetV2\u2013VGG16 model topped all single-model architectures, according to experimental results. The results show that hybrid transfer learning approaches can offer a potential option for computer-aided diagnosis systems and more consistent and reliable classification performance for breast MRI-based cancer detection.<\/jats:p>","DOI":"10.1186\/s12911-026-03557-3","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T10:05:26Z","timestamp":1779271526000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A hybrid deep learning framework for accurate breast cancer classification using MRI images"],"prefix":"10.1186","volume":"26","author":[{"given":"Melahat Sevg\u00fcl","family":"Bakay","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"3557_CR1","doi-asserted-by":"crossref","unstructured":"Bray F et al. 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