Aug 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 49 references
TL;DR
Experiments on two datasets suggest the potential utility of MulGM-Net for joint tumor delineation and malignancy assessment in head-and-neck and breast imaging, while the limited dataset size and the absence of external validation indicate that broader confirmation is still required.
MRI brain tumor analysis is a crucial yet time-consuming clinical task often affected by inter-observer variability and the limited availability of annotated datasets. This study proposes RadioOneAI, a unified deep learning framework that combines synthetic data generation, tumor classification, detection, and segmenta...
Nipuna Dassanayake, Y. Dissanayake, Shakya Pinnawala et al.· International Conference on...· 0 citations
Existing multimodal approaches for breast cancer classification rely on fixed-stage fusion, where clinical features are incorporated as static auxiliary inputs, limiting dynamic interactions between visual and clinical information. Furthermore, reported performance gains in this literature are rarely validated under le...
Deema A. Alzamil, B. Alkhamees· IEEE Access· 0 citations
NeuroAttnFuseNet, a dual-path attention-guided fusion network for four-class MRI image classification into glioma, meningioma, pituitary, and no tumor, is proposed, indicating high image-level performance on the merged dataset.
Md Sanowar Hossain Sabuj, F. Sultana, Mahum Rashid et al.· Discover Artificial Intellig...· 0 citations
The findings demonstrate the potential of combining complementary transfer-learning models with preprocessing, ensemble fusion, and explainable artificial intelligence for automated brain tumor classification from MRI images.
Ahmed Thijeel· Alkadhim Journal for Compute...· 0 citations
Brain tumors represent some of the most aggressive malignancies, MRI imaging is the main tool used by clinicians for brain tumor diagnosis. Accurate segmentation is essential for clinical diagnosis. Manual methods are labor-intensive and prone to inter-observer variability, motivating automated alternatives. Transforme...
Aalia Bello, Zhao Zhang, F. Boukari· Applications of Machine Lear...· 0 citations
Ovarian cancer remains a leading cause of gynaecological cancer mortality, and ultrasound-based deep learning systems for its diagnosis are typically built as separate post hoc processes for classification, segmentation, and interpretability, which introduces workflow inefficiencies and may produce inconsistent predict...
Abdussamad Abdullahi Musa, D. Emmanuel, Adeeb Alchaikh Hassan et al.· Electronics· 0 citations
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