Deep Learning-Based Detection, Classification, and Segmentation of Cerebral Microbleeds

Artificial intelligence (AI) has increasingly been integrated into various fields of medicine, necessitating rigorous oversight by scientists to ensure its appropriate application. This study presents an analysis of microhemorrhages, which are closely associated with significant cerebrovascular events. Researchers have designed a study to investigate the feasibility of utilizing AI in the detection and assessment of microhemorrhages.

The study retrospectively analyzed 108 patients with cerebral microhemorrhagic foci and 108 controls using cranial magnetic resonance imaging (MRI) and susceptibility-weighted sequences, following ethical approval. Deep learning models, including a customized DenseNet for classification and nnU-Net for segmentation, were trained on preprocessed MRI data to detect and localize microhemorrhages efficiently.

The study involved a total of 216 patients with and without cerebral microbleeds. There were a total of 146 foci in the 108 patient group. The classification model demonstrated an area under the curve of 80% ± 2.7% and an accuracy of 75% ± 1.7%, though specificity was more limited at 68% ± 4.3%. The segmentation model identified microhemorrhagic regions with a sensitivity of 89.4% ± 1.4% and achieved a Dice similarity coefficient of 62.05% ± 2.3%.

Overall, both models demonstrated the potential for detecting and localizing microhemorrhages in 3D MRI data, though the classification model showed moderate specificity. The findings suggest that AI-based approaches may eventually serve as a supportive tool for microhemorrhage detection, provided further refinements are made to enhance detection accuracy and clinical applicability. Cite this article as: Akkaya HE, Polat Ö, Çolakoğlu H, Akpinar Dİ, Kaya E. Deep learning-based detection, classification, and segmentation of cerebral microbleeds. Eurasian J Med. 2026, 58(4), 1414, doi: 10.5152/eurasianjmed.2026.261414.